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1. Variability of Precipitating Ion Fluxes During the September 2017 Event at Mars

Author

Jasper Halekas, Takuya Hara, Ronan Modolo, O. Witasse, Antoine Martinez, J. P. McFadden, Norberto Romanelli, Francis G. Eparvier, Mats Holmström, Robert Lillis, Y. J. Ma, Shannon Curry, Janet G. Luhmann, François Leblanc, Jean-Yves Chaufray, Bruce M. Jakosky, John E. P. Connerney, Davin Larson, HELIOS - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Space Sciences Laboratory [Berkeley] (SSL), University of California [Berkeley], University of California-University of California, Department of Physics and Astronomy [Iowa City], University of Iowa [Iowa City], Laboratory for Atmospheric and Space Physics [Boulder] (LASP), University of Colorado [Boulder], NASA Goddard Space Flight Center (GSFC), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Department of Earth, Planetary and Space Sciences [Los Angeles] (EPSS), University of California [Los Angeles] (UCLA), Swedish Institute of Space Physics [Uppsala] (IRF), European Space Research and Technology Centre (ESTEC), and European Space Agency (ESA)

Subjects
[SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], 010504 meteorology & atmospheric sciences, Spectrometer, Astrophysics::High Energy Astrophysical Phenomena, Magnetosphere, Mars Exploration Program, Space weather, Atmospheric sciences, 01 natural sciences, [PHYS.PHYS.PHYS-SPACE-PH]Physics [physics]/Physics [physics]/Space Physics [physics.space-ph], Atmosphere, Solar wind, Geophysics, 13. Climate action, Space and Planetary Science, Physics::Space Physics, Coronal mass ejection, Astrophysics::Solar and Stellar Astrophysics, Environmental science, Dynamic pressure, Astrophysics::Earth and Planetary Astrophysics, Physics::Atmospheric and Oceanic Physics, 0105 earth and related environmental sciences
Abstract

International audience; In this work, we study the influence of the September 2017 solar event on the precipitating heavy ion fluxes towards Mars' atmosphere as seen by MAVEN/SWIA, an energy and angular ion spectrometer and by MAVEN/STATIC, an energy, mass and angular ion spectrometer. After a careful reconstruction of the background induced by the Solar Energetic Particle (SEP) event in the MAVEN/SWIA spectrometer, we investigate the precipitating ion flux responses to the space weather events that took place in September 2017. This period is a unique opportunity to analyze the respective role of various possible drivers of heavy ion precipitation into Mars’ atmosphere with a wide range of different space weather events occurring during the same month. This study shows an increase in the precipitation flux by more than one order of magnitude during the arrival of the September Interplanetary Coronal Mass Ejection (ICME) compared to the average flux during quiet solar conditions. We also showed that among the possible solar drivers, the solar wind dynamic pressure is the most significant during September 2017.

Published
2019
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2. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Author

Cury, Claire, Durrleman, Stanley, Cash, David, Lorenzi, Marco, Nicholas, Jennifer, Bocchetta, Martina, Van Swieten, John, Borroni, Barbara, Galimberti, Daniela, Masellis, Mario, Tartaglia, Maria Carmela, Rowe, James, Graff, Caroline, Tagliavini, Fabrizio, Frisoni, Giovanni, Laforce, Robert, Finger, Elizabeth, de Mendonça, Alexandre, Sorbi, Sandro, Ourselin, Sébastien, Rohrer, Jonathan, Modat, Marc, Andersson, Christin, Archetti, Silvana, Arighi, Andrea, Benussi, Luisa, Black, Sandra, Cosseddu, Maura, Fallstrm, Marie, Ferreira, Carlos, Fenoglio, Chiara, Fox, Nick, Freedman, Morris, Fumagalli, Giorgio, Gazzina, Stefano, Ghidoni, Robert, Grisoli, Marina, Jelic, Vesna, Jiskoot, Lize, Keren, Ron, Lombardi, Gemma, Maruta, Carolina, Meeter, Lieke, van Minkelen, Rick, Nacmias, Benedetta, Ijerstedt, Linn, Padovani, Alessandro, Panman, Jessica, Pievani, Michela, Polito, Cristina, Premi, Enrico, Prioni, Sara, Rademakers, Rosa, Redaelli, Veronica, Rogaeva, Ekaterina, Rossi, Giacomina, Rossor, Martin, Scarpini, Elio, Tang-Wai, David, Thonberg, Hakan, Tiraboschi, Pietro, Verdelho, Ana, Warren, Jason, Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Dementia Research Centre [London] (DRC), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), London School of Hygiene and Tropical Medicine (LSHTM), Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Brescia, Centro Dino Ferrari [Milano], Università degli Studi di Milano = University of Milan (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Sunnybrook Research Institute [Toronto] (SRI), Sunnybrook Health Sciences Centre, Tanz Center Research in Neurodegenerative Diseases [Toronto], University of Toronto, University of Cambridge [UK] (CAM), Karolinska Institutet [Stockholm], Karolinska University Hospital [Stockholm], Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Centro San Giovanni di Dio, Fatebenefratelli, Brescia (IRCCS), Università degli Studi di Brescia = University of Brescia (UniBs), Université Laval [Québec] (ULaval), University of Western Ontario (UWO), Faculdade de Medicina [Lisboa], Universidade de Lisboa = University of Lisbon (ULISBOA), Università degli Studi di Firenze = University of Florence (UniFI), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Civic Hospital of Brescia, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, IRCCS Fatebenefratelli - Brescia, Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), UCL, Institute of Neurology [London], Toronto Western Hospital, Neuroimaging and Telemedicine (LENITEM), Mayo Clinic [Jacksonville], Marco Lorenzi received funding from the EPSRC (EP/J020990/1). Jennifer Nicholas is supported by UK Medical Research Council (grant MR/M023664/1). David Cash is supported by grants from the Alzheimer Society(AS-PG-15-025), Alzheimers Research UK (ARUK-PG2014-1946) and Medical Research Council UK (MR/M023664/1). JBR is supported by the Wellcome Trust (103838). Jonathan D. Rohrer is an MRC Clinician Scientist and has received funding from the NIHR Rare Diseases Translational Research Collaboration. Se361 bastien Ourselin receives funding from the EPSRC (EP/H046410/1, EP/K005278), the MRC (MR/J01107X/1), the NIHR Biomedical Research Unit (Dementia) at UCL and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative- BW.mn.BRC10269). Marc Modat is supported by the UCL Leonard Wolfson Experimental Neurology Centre, ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), European Project: 601055,EC:FP7:ICT,FP7-ICT-2011-9,VPH-DARE@IT(2013), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), Cury, Claire, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Idex UCA JEDI - - UCA JEDI2015 - ANR-15-IDEX-0001 - IDEX - VALID, VPH Dementia Research Enabled by IT - VPH-DARE@IT - - EC:FP7:ICT2013-04-01 - 2017-03-31 - 601055 - VALID, Data-driven models for Progression Of Neurological Disease - EuroPOND - - H2020 Pilier Societal Challenges2016-01-01 - 2019-12-31 - 666992 - VALID, Learning spatiotemporal patterns in longitudinal image data sets of the aging brain - LEASP - - H2020 ERC2016-09-01 - 2021-08-31 - 678304 - VALID, Neurology, Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano [Milano] (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Università degli Studi di Brescia [Brescia], Universidade de Lisboa (ULISBOA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, Prodromal Symptoms, Neuroimaging, Middle Aged, Magnetic Resonance Imaging, Article, Shape analysis, Clustering, Cohort Studies, Computational anatomy, Spatio-Temporal Analysis, Thalamus, Frontotemporal Dementia, ddc:618.97, Parallel transport, Spatiotemporal geodesic regression, Humans, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease., Graphical abstract Image 1, Highlights • Clustering shape parametrisation allows local shape analysis. • Thalamic shape changes appear 5 years before onset of fronto temporal dementia. • Shape changes seem to occur before volume changes. • Pre-symptomatic shape changes in thalamus are dorsofrontal, where connecting to temporal lobes.

Published
2019
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3. Anomaly detection for the individual analysis of brain PET images

Author

Sebastien Ourselin, M. Jorge Cardoso, Marie-Odile Habert, Jorge Samper-González, Ninon Burgos, Stanley Durrleman, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre d'Acquisition et de Traitement des Images [Paris], Commissariat à l'énergie atomique et aux énergies alternatives (CEA), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), European Project: 609102,EC:FP7:PEOPLE,FP7-PEOPLE-2013-COFUND,PRESTIGE(2014), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Burgos, Ninon, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Learning spatiotemporal patterns in longitudinal image data sets of the aging brain - LEASP - - H2020 ERC2016-09-01 - 2021-08-31 - 678304 - VALID, PRES Towards International Gain of Excellence - PRESTIGE - - EC:FP7:PEOPLE2014-09-01 - 2019-08-31 - 609102 - VALID, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Positron emission tomography, Image Processing, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Image registration, Anomaly detection, Brain mapping, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Dementia, Radiology, Nuclear Medicine and imaging, medicine.diagnostic_test, business.industry, Pattern recognition, Image synthesis, medicine.disease, 030220 oncology & carcinogenesis, Artificial intelligence, Abnormality, business, Frontotemporal dementia
Abstract

International audience; Purpose:In clinical practice, positron emission tomography (PET) images are mostly analysed visually, but the sensitivity and specificity of this approach greatly depends on the observer's experience. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings. We present an anomaly detection framework for the individual analysis of PET images. Approach: We created subject-specific abnormality maps that summarise the pathology's topographical distribution in the brain by comparing the subject's PET image to a model of healthy PET appearance that is specific to the subject under investigation. This model was generated from demographically and morphologically-matched PET scans from a control dataset.Results: We generated abnormality maps for healthy controls, patients at different stages of Alzheimer's disease and with different frontotemporal dementia syndromes. We showed that no anomalies were detected for the healthy controls and that the anomalies detected from the patients with dementia coincided with the regions where abnormal uptake was expected. We also validated the proposed framework using the abnormality maps as inputs of a classifier and obtained higher classification accuracies than when using the PET images themselves as inputs. Conclusions: The proposed method was able to automatically locate and characterise the areas characteristic of dementia from PET images. The abnormality maps are expected to i) help clinicians in their diagnosis by highlighting, in a data-driven fashion, the pathological areas, and ii) improve the interpretability of subsequent analyses, such as computer-aided diagnosis or spatio-temporal modelling.

Published
2021
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4. Midterm Outcomes of BeGraft Stent Grafts Used as Bridging Stents in Fenestrated Endovascular Aortic Aneurysm Repair

Author

Rachel E. Clough, Rafaëlle Spear, Justine Mougin, Thomas Le Houérou, Dominique Fabre, Jonathan Sobocinski, Stéphan Haulon, Université de Lille, Inserm, CHU Lille, Imaging Sciences and Biomedical Engineering Division [London], Centre Hospitalier Universitaire [Grenoble] [CHU], Advanced Drug Delivery Systems (ADDS) - U1008, Pôle des Cardiopathies Congénitales du Nouveau-Né à L'adulte - Centre Constitutif Cardiopathies Congénitales Complexes M3C, Groupe Hospitalier Paris Saint-Joseph, Hôpital Marie-Lannelongue, Inserm U999, Université Paris-Saclay, Guy's and St Thomas' Hospital [London]-King‘s College London, Centre Hospitalier Universitaire [Grenoble] (CHU), Médicaments et biomatériaux à libération contrôlée: mécanismes et optimisation - Advanced Drug Delivery Systems - U 1008 (MBLC - ADDS), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)

Subjects
aorta, bridging stent, BeGraft, Bentley, fenestrated, aneurysm, [SDV]Life Sciences [q-bio], Radiology, Nuclear Medicine and imaging, Surgery, Cardiology and Cardiovascular Medicine
Abstract

Purpose: Fenestrated endovascular aneurysm repair (fEVAR) is established for the treatment of juxtarenal, pararenal, and thoracoabdominal aortic aneurysms (TAAAs). Bridging stents are used to connect the main body of the stent graft to the aortic branch vessels. Complications related to the bridging stents compromise the durability of the repair and require urgent re-intervention. Here we present the midterm results of the BeGraft stent graft system used for fEVAR. Materials and method: All consecutive patients treated with fEVAR and the current BeGraft Peripheral Stent Graft between November 2015 and September 2016 were included. Results: Thirty-nine consecutive patients (38 men) were enrolled and 101 BeGraft second-generation stent grafts were implanted. The median aneurysm diameter was 60 mm (54.5–67.0 mm). Aneurysms were juxtarenal and pararenal (19/39, 48.1%), type 4 TAAA (3/39, 7.7%), type 1, 2, and 3 TAAA (7/39, 17.8%), type 5 TAAA (4/39, 10.2%), and 15.4% (6/39) had a type I endoleak following a previous EVAR. Fifty-five BeGrafts were implanted in mesenteric arteries (22 in coeliac trunks, 31 in the superior mesenteric artery, and 2 in a hepatic or splenic artery) and 46 into renal arteries (24 right and 22 left). The renal artery diameters were 5, 6, 7, and 8 mm in 9, 7, 26, and 4 patients, respectively. Mesenteric arteries were exclusively stented with 9 and 10 mm diameter devices. The median follow-up was 33 months (IQ25 17–IQ75 36). During follow-up, 11 patients died (28%) from non–aneurysm-related causes. The overall patency rates for bridging stents were 98% and 97% at 1 and 2 years, respectively, with a freedom from secondary procedure rate on BeGraft stent grafts of 96% (97/101). All events occurred on stents implanted in renal arteries. Conclusion: Early favorable outcomes are confirmed during longer term follow-up. Vigilant surveillance is required.

Published
2022
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5. A flexible framework for sequential estimation of model parameters in computational hemodynamics

Author

Christopher J. Arthurs, Tobias Schaeffter, C. Alberto Figueroa, Philippe Moireau, Nan Xiao, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Technical University of Berlin / Technische Universität Berlin (TU), Physikalisch-Technische Bundesanstalt [Berlin] (PTB), Depts. of Surgery and Biomedical Engineering, University of Michigan, École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, and Technical University Berlin

Subjects
Computer science, Calibration (statistics), 0206 medical engineering, Computational hemodynamics, 02 engineering and technology, Patient specific modeling, Synthetic data, lcsh:TA168, Stiffness, 03 medical and health sciences, 0302 clinical medicine, Data assimilation, Parameter estimation, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Engineering (miscellaneous), Sequential estimation, Boundary conditions, Estimation theory, Applied Mathematics, Kalman filter, 020601 biomedical engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, Flow (mathematics), lcsh:Systems engineering, Modeling and Simulation, Netlist, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], lcsh:Mechanics of engineering. Applied mechanics, lcsh:TA349-359, Algorithm, Kalman filtering, 030217 neurology & neurosurgery, Research Article
Abstract

A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

Published
2020
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6. Monitoring of cardiovascular physiology augmented by a patient-specific biomechanical model during general anesthesia. A proof of concept study

Author

Le Gall, Arthur, Vallée, Fabrice, Pushparajah, Kuberan, Hussain, Tarique, Mebazaa, Alexandre, Chapelle, Dominique, Gayat, Étienne, Chabiniok, Radomír, Service d'Anesthésie-Réanimation [AP-HP Hôpitaux Saint-Louis Lariboisière], Université de Paris-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Lariboisière, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, University of Texas Southwestern Medical Center [Dallas], School of Biomedical Engineering & Imaging Sciences [London], Department of Mathematics [Prague] (FNSPE), Czech Technical University in Prague (CTU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université de Paris (UP), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Cité (UPCité), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and ANAESTASSIST

Subjects
Male, Blood Pressure, Vascular Medicine, Biochemistry, Stiffness, Norepinephrine, Catecholamines, Medicine and Health Sciences, Vasoconstrictor Agents, Prospective Studies, Amines, Cardiac Output, Aorta, Organic Compounds, Simulation and Modeling, Models, Cardiovascular, Heart, Neurochemistry, Neurotransmitters, Middle Aged, Systolic Pressure, Biomechanical Phenomena, Chemistry, Physical Sciences, Medicine, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Female, Anatomy, Hypotension, Algorithms, Research Article, Biogenic Amines, Science, Materials Science, Material Properties, Bioenergetics, Anesthesia, General, Research and Analysis Methods, Proof of Concept Study, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Mechanical Properties, Humans, Arterial Pressure, Organic Chemistry, Hemodynamic Monitoring, Chemical Compounds, Biology and Life Sciences, Stroke Volume, Hormones, Cardiovascular Anatomy, Blood Vessels, Neuroscience
Abstract

International audience; During general anesthesia (GA), direct analysis of arterial pressure or aortic flow waveforms may be inconclusive in complex situations. Patient-specific biomechanical models, based on data obtained during GA and capable to perform fast simulations of cardiac cycles, have the potential to augment hemodynamic monitoring. Such models allow to simulate Pressure-Volume (PV) loops and estimate functional indicators of cardiovascular (CV) system, e.g. ventricular-arterial coupling (Vva), cardiac efficiency (CE) or myocardial contractility, evolving throughout GA. In this prospective observational study, we created patient-specific biomechanical models of heart and vasculature of a reduced geometric complexity for n=45 patients undergoing GA, while using transthoracic echocardiography and aortic pressure and flow signals acquired in the beginning of GA (baseline condition). If intraoperative hypotension (IOH) appeared, diluted norepinephrine (NOR) was administered and the model readjusted according to the measured aortic pressure and flow signals. Such patients were a posteriori assigned into a so-called hypotensive group. The accuracy of simulated mean aortic pressure (MAP) and stroke volume (SV) at baseline were in accordance with the guidelines for the validation of new devices or reference measurement methods in all patients. After NOR administration in the hypotensive group, the percentage of concordance with 10% exclusion zone between measurement and simulation was > 95% for both MAP and SV. The modeling results showed a decreased Vva (0.64±0.37 vs 0.88±0.43; p=0.039) and an increased CE (0.8±0.1 vs 0.73±0.11; p=0.042) in hypotensive vs normotensive patients. Furthermore, Vva increased by 92±101%, CE decreased by 13±11% (p < 0.001 for both) and contractility increased by 14±11% (p = 0.002) in the hypotensive group post-NOR administration. In this work we demonstrated the application of fast-running patient-specific biophysical models to estimate PV loops and functional indicators of CV system using clinical data available during GA. The work paves the way for model-augmented hemodynamic monitoring at operating theatres or intensive care units to enhance the information on patient-specific physiology.

Published
2020
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7. A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations

Author

Karli Gillette, Gernot Plank, Marina Strocchi, Christopher A. Rinaldi, Orod Razeghi, Edward J. Vigmond, Justin Gould, Matthias A. F. Gsell, Martin J. Bishop, Aurel Neic, Baldeep S. Sidhu, Elias Karabelas, Anton J. Prassl, Jonathan M. Behar, Christoph M. Augustin, Steven A. Niederer, King‘s College London, Medical University Graz, Modélisation et calculs pour l'électrophysiologie cardiaque (CARMEN), IHU-LIRYC, Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IHU-LIRYC, and Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-CHU Bordeaux [Bordeaux]

Subjects
Male, Computer science, 030204 cardiovascular system & hematology, Ventricular Function, Left, 030218 nuclear medicine & medical imaging, Diagnostic Radiology, Cohort Studies, 0302 clinical medicine, Medicine and Health Sciences, Pulmonary Arteries, Tomography, ComputingMilieux_MISCELLANEOUS, Aorta, Aged, 80 and over, Computational model, Multidisciplinary, Ejection fraction, Orientation (computer vision), Radiology and Imaging, Simulation and Modeling, Heart, Stroke volume, Arteries, Middle Aged, Medicine, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Female, Anatomy, Research Article, Adult, Cardiac Ventricles, Imaging Techniques, Science, Heart Ventricles, Neuroimaging, Research and Analysis Methods, Veins, 03 medical and health sciences, Diagnostic Medicine, medicine.artery, medicine, Humans, Polygon mesh, Computer Simulation, Endocardium, Aged, Heart Failure, Myocardium, Cardiac Ventricle, Biology and Life Sciences, Surgical Mesh, medicine.disease, Computed Axial Tomography, Heart failure, Ventricular Function, Right, Cardiovascular Anatomy, Blood Vessels, Biomedical engineering, Neuroscience
Abstract

Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 6714 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.10.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60 and 80 at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.390.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 14916ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 351% and 302%, respectively, and a LV and RV stroke volume (SV) of 9528mL and 6511mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications. (VLID)5215097 Version of record

Published
2020
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8. Simulating ventricular systolic motion in a four-chamber heart model with spatially varying robin boundary conditions to model the effect of the pericardium

Author

Matthias A. F. Gsell, Martin J. Bishop, Caroline H. Roney, Edward J. Vigmond, Jonathan M. Behar, Christoph M. Augustin, Anton J. Prassl, Justin Gould, Steven A. Niederer, Gernot Plank, Christopher A. Rinaldi, Marina Strocchi, Orod Razeghi, King‘s College London, Medical University Graz, Modélisation et calculs pour l'électrophysiologie cardiaque (CARMEN), IHU-LIRYC, Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IHU-LIRYC, Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-CHU Bordeaux [Bordeaux], and Agence Spatiale Européenne = European Space Agency (ESA)

Subjects
Cardiac function curve, Materials science, Systole, 0206 medical engineering, Biomedical Engineering, Biophysics, Cardiac electromechanics, Heart failure, 02 engineering and technology, Article, Ventricular epicardium, 03 medical and health sciences, 0302 clinical medicine, Cardiac motion, medicine, Humans, Ventricular Function, Pericardium, Orthopedics and Sports Medicine, cardiovascular diseases, Isovolumetric contraction, integumentary system, Ventricular systolic motion, Rehabilitation, Models, Cardiovascular, Apico-basal shortening, Stiffness, Computer models, Mechanics, medicine.disease, Myocardial Contraction, 020601 biomedical engineering, Robin boundary condition, medicine.anatomical_structure, cardiovascular system, [SDV.IB]Life Sciences [q-bio]/Bioengineering, medicine.symptom, tissues, 030217 neurology & neurosurgery
Abstract

International audience; The pericardium affects cardiac motion by limiting epicardial displacement normal to the surface. In computational studies, it is important for the model to replicate realistic motion, as this affects the physiological fidelity of the model. Previous computational studies showed that accounting for the effect of the pericardium allows for a more realistic motion simulation. In this study, we describe the mechanism through which the pericardium causes improved cardiac motion. We simulated electrical activation and contraction of the ventricles on a four-chamber heart in the presence and absence of the effect of the pericardium. We simulated the mechanical constraints imposed by the pericardium by applying normal Robin boundary conditions on the ventricular epicardium. We defined a regional scaling of normal springs stiffness based on image-derived motion from CT images. The presence of the pericardium reduced the error between simulated and image-derived end-systolic configurations from 12.8±4.1 mm to 5.7±2.5 mm. First, the pericardium prevents the ventricles from spherising during isovolumic contraction, reducing the outward motion of the free walls normal to the surface and the upwards motion of the apex. Second, by restricting the inward motion of the free and apical walls of the ventricles the pericardium increases atrioventricular plane displacement by four folds during ejection. Our results provide a mechanistic explanation of the importance of the pericardium in physiological simulations of electromechanical cardiac function.

Published
2020
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9. Exploring kinetic energy as a new marker of cardiac function in the single ventricle circulation

Author

Reza Razavi, Daniel Giese, Kuberan Pushparajah, David S. Celermajer, Tobias Schaeffter, Eva Sammut, Radomir Chabiniok, Tarique Hussain, James Wong, Shane M. Tibby, Gerald F. Greil, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Saclay, University of Texas Southwestern Medical Center [Dallas], School of Biomedical Engineering & Imaging Sciences (BMEIS), King's College London, UK, University of Texas Southwestern Medical Center Dallas, USA (Inria associate team ToFMOD), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris

Subjects
Male, Aging, Physiology, heart failure, 030204 cardiovascular system & hematology, Ventricular Function, Left, 030218 nuclear medicine & medical imaging, 0302 clinical medicine, Medicine, Child, Ejection fraction, Middle Aged, Magnetic Resonance Imaging, congenital heart disease, Healthy Volunteers, medicine.anatomical_structure, Child, Preschool, Blood Circulation, Cardiology, Female, medicine.symptom, Algorithms, Adult, Heart Defects, Congenital, Cardiac function curve, medicine.medical_specialty, Adolescent, Heart Ventricles, CIRCULATORY FAILURE, Asymptomatic, Article, cardiac magnetic resonance, Young Adult, 03 medical and health sciences, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Physiology (medical), Internal medicine, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Humans, In patient, Systole, Aged, business.industry, Infant, Reproducibility of Results, Stroke Volume, medicine.disease, Ventricle, Heart failure, Energy Metabolism, business, Biomarkers
Abstract

Ventricular volumetric ejection fraction (VV EF) is often normal in patients with single ventricle circulations despite them experiencing symptoms related to circulatory failure. We sought to determine if kinetic energy (KE) could be a better marker of ventricular performance. KE was prospectively quantified using four-dimensional flow MRI in 41 patients with a single ventricle circulation (aged 0.5–28 yr) and compared with 43 healthy volunteers (aged 1.5–62 yr) and 14 patients with left ventricular (LV) dysfunction (aged 28–79 yr). Intraventricular end-diastolic blood was tracked through systole and divided into ejected and residual blood components. Two ejection fraction (EF) metrics were devised based on the KE of the ejected component over the total of both the ejected and residual components using 1) instantaneous peak KE to assess KE EF or 2) summating individual peak particle energy (PE) to assess PE EF. KE EF and PE EF had a smaller range than VV EF in healthy subjects (97.9 ± 0.8 vs. 97.3 ± 0.8 vs. 60.1 ± 5.2%). LV dysfunction caused a fall in KE EF ( P = 0.01) and PE EF ( P = 0.0001). VV EF in healthy LVs and single ventricle hearts was equivalent; however, KE EF and PE EF were lower ( P < 0.001) with a wider range indicating a spectrum of severity. Those reporting the greatest symptomatic impairment (New York Heart Association II) had lower PE EF than asymptomatic subjects ( P = 0.0067). KE metrics are markers of healthy cardiac function. PE EF may be useful in grading dysfunction. NEW & NOTEWORTHY Kinetic energy (KE) represents the useful work of the heart in ejecting blood. This article details the utilization of KE indexes to assess cardiac function in health and a variety of pathophysiological conditions. KE ejection fraction and particle energy ejection fraction (PE EF) showed a narrow range in health and a lower wider range in disease representing a spectrum of severity. PE EF was altered by functional status potentially offering the opportunity to grade dysfunction.

Published
2018
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10. Non-volatile RF and mm-wave switches based on monolayer hBN

Author

Jack C. Lee, Vanessa Avramovic, Xiaohan Wu, Ruijing Ge, Henri Happy, Deji Akinwande, Myungsoo Kim, Emiliano Pallecchi, Etienne Okada, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Carbon-IEMN (CARBON-IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Paul Scherrer Institute, Electrochemistry Laboratory, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Renatech Network, and Carbon - IEMN (CARBON - IEMN)

Subjects
Materials science, Terahertz radiation, 02 engineering and technology, Memristor, 7. Clean energy, law.invention, microwave switches, 03 medical and health sciences, [SPI]Engineering Sciences [physics], RF switch, molybdenum, law, Monolayer, 0202 electrical engineering, electronic engineering, information engineering, Insertion loss, 030304 developmental biology, Microelectromechanical systems, 0303 health sciences, business.industry, molybdenum compounds, 020206 networking & telecommunications, Nanosecond, Cutoff frequency, random-access storage, millimetre wave integrated circuits, Optoelectronics, low-power electronics, business, boron compounds
Abstract

International audience; Non-volatile radio-frequency (RF) switches based on hexagonal boron nitride (hBN) are realized for the first time with low insertion loss (≤ 0.2 dB) and high isolation (≥ 15 dB) up to 110 GHz. Crystalline hBN enables the thinnest RF switch device with a single monolayer (~0.33 nm) as the memory layer owing to its robust layered structure. It affords ~20 dBm power handling, 10 dB higher compared to MoS 2 switches due to its wider bandgap (~6 eV). Importantly, operating frequencies cover the RF, 5G, and mm-wave bands, making this a promising low-power switch for diverse communication and connectivity front-end systems. Compared to other switch technologies based on MEMS, memristor, and phase-change memory (PCM), hBN switches offer a promising combination of non-volatility, nanosecond switching, power handling, high figure-of-merit cutoff frequency (43 THz), and heater-less ambient integration. Our pioneering work suggests that atomically-thin nanomaterials can be good device candidates for 5G and beyond.

Published
2019
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11. Verification of the Coupled-Momentum Method with Womersley's Deformable Wall Analytical Solution

Author

Christopher J. Arthurs, Vasilina Filonova, Irene E. Vignon-Clementel, C. Alberto Figueroa, University of Michigan Medical School [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Numerical simulation of biological flows (REO), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), and This work was supported bythe National Institute of Health [grant U01 HL135842]

Subjects
Fluid-Structure Interaction, 0206 medical engineering, Biomedical Engineering, Pulsatile flow, fluid-structure interaction, 02 engineering and technology, Kinematics, 030204 cardiovascular system & hematology, Domain (mathematical analysis), Article, Momentum, 03 medical and health sciences, 0302 clinical medicine, impedance boundary condition, Blood Flow, Fluid–structure interaction, Scale analysis (mathematics), blood flow, Animals, Humans, Coupled-Momentum Method, [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], Molecular Biology, Physics, Impedance Boundary Condition, [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Mechanics of the fluids [physics.class-ph], Applied Mathematics, Attenuation, Linear elasticity, Verification, Mechanics, 020601 biomedical engineering, Womersley Deformable Wall Solution, Carotid Arteries, Computational Theory and Mathematics, Modeling and Simulation, Pulsatile Flow, Blood Circulation, Womersley Deformable Wall solution, verification, coupled-momentum method, Software, Algorithms, Blood Flow Velocity
Abstract

International audience; In this paper, we perform a verification study of the Coupled-Momentum Method (CMM), a 3D fluid-structure interaction (FSI) model which uses a thin linear elastic membrane and linear kinematics to describe the mechanical behavior of the vessel wall. The verification of this model is done using Womersley's deformable wall analytical solution for pulsatile flow in a semi-infinite cylindrical vessel. This solution is, under certain premises, the analytical solution of the CMM and can thus be used for model verification. For the numerical solution, we employ an impedance boundary condition to define a reflection-free outflow boundary condition and thus mimic the physics of the analytical solution, which is defined on a semi-infinite domain. We first provide a rigorous derivation of Womersley's deformable wall theory via scale analysis. We then illustrate different characteristics of the analytical solution such as space-time wave periodicity and attenuation. Finally, we present the verification tests comparing the CMM with Womersley's theory.

Published
2019
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12. Model-based indices of early-stage cardiovascular failure and its therapeutic management in Fontan patients

Author

Radomir Chabiniok, Bram Ruijsink, Konrad Zugaj, Kuberan Pushparajah, King‘s College London, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’sCollege London and the NIHR Cardiovascular MedTech Co-operative (previously existing as the Cardiovascular Healthcare Technology Co-operative 2012–2017), the support of Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], and from the Inria Associated team ToFMOD. The views expressed are those of the author(s)and not necessarily those of the NHS, the NIHR or the Department of Health. In addition, the authors would like to acknowledge Philippe Moireau and Dominique Chapelle, Inria research team MΞDISIM, for providing the cardiac simulation software CardiacLab under a Royalty-fee software lice., École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Ozenne, Valéry, Vigmond, Edward, Coudière, Yves, and Zemzemi, Nejib

Subjects
medicine.medical_specialty, congenital, hereditary, and neonatal diseases and abnormalities, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Heart failure, 030204 cardiovascular system & hematology, 01 natural sciences, Fontan circulation, Contractility, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Internal medicine, Medicine, Pulmonary vascular resistance, 0101 mathematics, Stage (cooking), Dobutamine stress, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, medicine.disease, 3. Good health, Cardiovascular physiology, 010101 applied mathematics, Pulmonary vasodilation therapy, medicine.anatomical_structure, Vascular resistance, Cardiology, cardiovascular system, Biomechanical model, business
Abstract

International audience; Investigating the causes of failure of Fontan circulation in individual patients remains challenging despite detailed combined inva-sive cardiac catheterisation and magnetic resonance (XMR) exams at rest and during stress. In this work, we use a biomechanical model of the heart and Fontan circulation with the components of systemic and pulmonary beds to augment the diagnostic assessment of the patients undergoing the XMR stress exam. We apply our model in 3 Fontan patients and one biventricular "control" case. In all subjects, we obtained important biophysical factors of cardiovascular physiology-contractil-ity, contractile reserve and changes in systemic and pulmonary vascular resistance-which contribute to explaining the mechanism of failure in individual patients. Finally, we used the patient-specific model of one Fontan patient to investigate the impact of changes in pulmonary vas-cular resistance, aiming at in silico testing of pulmonary vasodilation treatments.

Published
2019
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13. Imaging localized neuronal activity at fast time scales through biomechanics

Author

Miklos Palotai, Katharina Schregel, Alexander Hammers, Sverre Holm, Sebastian Kozerke, Paul E. Barbone, Daniel Fovargue, Ralph Sinkus, Samuel Patz, Ben Fabry, Navid Nazari, David Nordsletten, Laboratoire de Recherche Vasculaire Translationnelle (LVTS (UMR_S_1148 / U1148)), Université Paris 13 (UP13)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Zurich, Patz, Samuel, Boston University [Boston] (BU), Biophysics Group Friedrich-Alexander University, Centre d'Etude et de Recherche Multimodal Et Pluridisciplinaire en imagerie du vivant (CERMEP - imagerie du vivant), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-CHU Grenoble-Hospices Civils de Lyon (HCL)-CHU Saint-Etienne-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institute for Biomedical Engineering [ETH Zürich] (IBT), Universität Zürich [Zürich] = University of Zurich (UZH)-Department of Information Technology and Electrical Engineering [Zürich] (D-ITET), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Physique des ondes pour la médecine, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV]Life Sciences [q-bio], 030218 nuclear medicine & medical imaging, 170 Ethics, Mice, 0302 clinical medicine, Thalamus, Forelimb, Premovement neuronal activity, Research Articles, ComputingMilieux_MISCELLANEOUS, Electric stimulation, Physics, Brain Mapping, Multidisciplinary, medicine.diagnostic_test, Biomechanics, SciAdv r-articles, Brain, Magnetic Resonance Imaging, Hindlimb, neuroscience, neuronal activity, Imaging, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Elastography, Preclinical imaging, Research Article, musculoskeletal diseases, animal structures, 610 Medicine & health, macromolecular substances, Stimulus (physiology), 03 medical and health sciences, medicine, Animals, 10237 Institute of Biomedical Engineering, 3101 Physics and Astronomy (miscellaneous), 1000 Multidisciplinary, technology, industry, and agriculture, Magnetic resonance imaging, equipment and supplies, Electric Stimulation, Mice, Inbred C57BL, Acoustic Stimulation, nervous system, Neuroscience, 030217 neurology & neurosurgery
Abstract

Mapping neuronal activity noninvasively is a key requirement for in vivo human neuroscience. Traditional functional magnetic resonance (MR) imaging, with a temporal response of seconds, cannot measure high-level cognitive processes evolving in tens of milliseconds. To advance neuroscience, imaging of fast neuronal processes is required. Here, we show in vivo imaging of fast neuronal processes at 100-ms time scales by quantifying brain biomechanics noninvasively with MR elastography. We show brain stiffness changes of ~10% in response to repetitive electric stimulation of a mouse hind paw over two orders of frequency from 0.1 to 10 Hz. We demonstrate in mice that regional patterns of stiffness modulation are synchronous with stimulus switching and evolve with frequency. For very fast stimuli (100 ms), mechanical changes are mainly located in the thalamus, the relay location for afferent cortical input. Our results demonstrate a new methodology for noninvasively tracking brain functional activity at high speed., Science Advances, 5 (4), ISSN:2375-2548

Published
2019
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14. Transfer learning from simulations on a reference anatomy for ECGI in personalized cardiac resynchronization therapy

Author

Jessica Webb, Jack Lee, Lauren Fovargue, Sophie Giffard-Roisin, Nicholas Ayache, Reza Razavi, Tom Jackson, Christopher A. Rinaldi, Hervé Delingette, Maxime Sermesant, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Division of Imaging Sciences, King‘s College London, SOFA, European Project: 611823,EC:FP7:ICT,FP7-ICT-2013-10,VP2HF(2013), and European Project: 715093,H2020,ERC-2016-STG,ECSTATIC(2017)

Subjects
Databases, Factual, Personalisation, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, medicine.medical_treatment, 0206 medical engineering, Biomedical Engineering, Cardiac resynchronization therapy, Cardiac electrophysiology, 02 engineering and technology, Inverse Problem of ECG, Cardiac Resynchronization Therapy, Electrocardiography, ECG imaging, personalisation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Image Processing, Computer-Assisted, Index Terms—Cardiac Electrophysiology, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Humans, Computer Simulation, Sinus rhythm, Precision Medicine, inverse problem of ECG, medicine.diagnostic_test, Body Surface Potential Mapping, In- verse Problem of ECG, Heart, Signal Processing, Computer-Assisted, Anatomy, Torso, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 020601 biomedical engineering, Cardiac Imaging Techniques, Identification (information), medicine.anatomical_structure, ECG Imaging, Cardiac Electrophysiology, Transfer of learning
Abstract

International audience; Goal: Non-invasive cardiac electrophysiology (EP) model personalisation has raised interest for instance in the scope of predicting EP cardiac resynchronization therapy (CRT) response. However, the restricted clinical applicability of current methods is due in particular to the limitation to simple situations and the important computational cost. Methods: We propose in this manuscript an approach to tackle these two issues. First, we analyse more complex propagation patterns (multiple onsets and scar tissue) using relevance vector regression and shape dimensionality reduction on a large simulated database. Second, this learning is performed offline on a reference anatomy and transferred onto patient-specific anatomies in order to achieve fast personalised predictions online. Results: We evaluated our method on a dataset composed of 20 dyssynchrony patients with a total of 120 different cardiac cycles. The comparison with a commercially available electrocardiographic imaging (ECGI) method shows a good identification of the cardiac activation pattern. From the cardiac parameters estimated in sinus rhythm, we predicted 5 different paced patterns for each patient. The comparison with the body surface potential mappings (BSPM) measured during pacing and the ECGI method indicates a good predictive power. Conclusion: We showed that learning offline from a large simulated database on a reference anatomy was able to capture the main cardiac EP characteristics from non-invasive measurements for fast patient-specific predictions. Significance: The fast CRT pacing predictions are a step forward to a non-invasive CRT patient selection and therapy optimisation, to help clinicians in these difficult tasks.

Published
2019
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15. Magnetic resonance elastography in nonlinear viscoelastic materials under load

Author

Dharmesh Patel, Adela Capilnasiu, Ralph Sinkus, David Nordsletten, Myrianthi Hadjicharalambous, Daniel Fovargue, Lynne E. Bilston, Hazel R. C. Screen, Ondrej Holub, Laboratoire de Recherche Vasculaire Translationnelle (LVTS (UMR_S_1148 / U1148)), Université Paris Diderot - Paris 7 (UPD7)-Université Paris 13 (UP13)-Institut National de la Santé et de la Recherche Médicale (INSERM), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Physique des ondes pour la médecine, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université Paris 13 (UP13)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Materials science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV]Life Sciences [q-bio], 0206 medical engineering, Context (language use), 02 engineering and technology, Models, Biological, Imaging phantom, Viscoelasticity, Tissue mechanics, Nonlinear mechanics, medicine, ComputingMilieux_MISCELLANEOUS, Original Paper, medicine.diagnostic_test, Phantoms, Imaging, Viscosity, Mechanical Engineering, Elastic waves, Stiffness, Mechanics, 020601 biomedical engineering, Magnetic Resonance Imaging, Elasticity, Magnetic resonance elastography, Biomechanical Phenomena, Nonlinear system, Nonlinear Dynamics, Modeling and Simulation, Polyvinyl Alcohol, Elasticity Imaging Techniques, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Elastography, Biorheology, medicine.symptom, Material properties, Rheology, Biotechnology
Abstract

Characterisation of soft tissue mechanical properties is a topic of increasing interest in translational and clinical research. Magnetic resonance elastography (MRE) has been used in this context to assess the mechanical properties of tissues in vivo noninvasively. Typically, these analyses rely on linear viscoelastic wave equations to assess material properties from measured wave dynamics. However, deformations that occur in some tissues (e.g. liver during respiration, heart during the cardiac cycle, or external compression during a breast exam) can yield loading bias, complicating the interpretation of tissue stiffness from MRE measurements. In this paper, it is shown how combined knowledge of a material’s rheology and loading state can be used to eliminate loading bias and enable interpretation of intrinsic (unloaded) stiffness properties. Equations are derived utilising perturbation theory and Cauchy’s equations of motion to demonstrate the impact of loading state on periodic steady-state wave behaviour in nonlinear viscoelastic materials. These equations demonstrate how loading bias yields apparent material stiffening, softening and anisotropy. MRE sensitivity to deformation is demonstrated in an experimental phantom, showing a loading bias of up to twofold. From an unbiased stiffness of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4910.4 \pm 635.8$$\end{document}4910.4±635.8 Pa in unloaded state, the biased stiffness increases to 9767.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm \,$$\end{document}±1949.9 Pa under a load of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx $$\end{document}≈ 34% uniaxial compression. Integrating knowledge of phantom loading and rheology into a novel MRE reconstruction, it is shown that it is possible to characterise intrinsic material characteristics, eliminating the loading bias from MRE data. The framework introduced and demonstrated in phantoms illustrates a pathway that can be translated and applied to MRE in complex deforming tissues. This would contribute to a better assessment of material properties in soft tissues employing elastography.

Published
2019
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16. Large cortical bone pores in the tibia are associated with proximal femur strength

Author

Iori, Gianluca, Schneider, Johannes, Reisinger, Andreas, Heyer, Frans, Peralta , Laura, Wyers, Caroline, Grasel, Melanie, Barkmann, Reinhard, Gluer, Claus C., van den Bergh, J.P., Pahr, Dieter, Raum, Kay, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Maastricht University Medical Centre (MUMC), Maastricht University [Maastricht], Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), King‘s College London, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Christian-Albrechts-Universität zu Kiel (CAU), Vienna University of Technology (TU Wien), Interne Geneeskunde, Promovendi NTM, and RS: NUTRIM - R3 - Respiratory & Age-related Health

Subjects
musculoskeletal diseases, Adult, Male, [SDV]Life Sciences [q-bio], Science, MICROARCHITECTURAL PARAMETERS, DISTAL RADIUS, Absorptiometry, Photon, BMD, Bone Density, Cortical Bone, Humans, FEMORAL FRACTURE LOAD, Aged, DXA, Aged, 80 and over, Tibia, Femur Neck, WOMEN, Correction, HIP FRACTURE, X-Ray Microtomography, Middle Aged, musculoskeletal system, HR-PQCT, Medicine, Female, Porosity, HIGH-RESOLUTION, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit, FRAGILITY
Abstract

International audience; Alterations of structure and density of cortical bone are associated with fragility fractures and can be assessed in vivo in humans at the tibia. Bone remodeling deficits in aging women have been recently linked to an increase in size of cortical pores. In this ex vivo study, we characterized the cortical microarchitecture of 19 tibiae from human donors (aged 69 to 94 years) to address, whether this can reflect impairments of the mechanical competence of the proximal femur, i.e., a major fracture site in osteoporosis. Scanning acoustic microscopy (12 μm pixel size) provided reference microstructural measurements at the left tibia, while the bone vBMD at this site was obtained using microcomputed tomography (microCT). The areal bone mineral density of both left and right femoral necks (aBMDneck) was measured by dual-energy X-ray absorptiometry (DXA), while homogenized nonlinear finite element models based on high-resolution peripheral quantitative computed tomography provided hip stiffness and strength for one-legged standing and sideways falling loads. Hip strength was associated with aBMDneck (r = 0.74 to 0.78), with tibial cortical thickness (r = 0.81) and with measurements of the tibial cross-sectional geometry (r = 0.48 to 0.73) of the same leg. Tibial vBMD was associated with hip strength only for standing loads (r = 0.59 to 0.65). Cortical porosity (Ct.Po) of the tibia was not associated with any of the femoral parameters. However, the proportion of Ct.Po attributable to large pores (diameter > 100 μm) was associated with hip strength in both standing (r = -0.61) and falling (r = 0.48) conditions. When added to aBMDneck, the prevalence of large pores could explain up to 17% of the femur ultimate force. In conclusion, microstructural characteristics of the tibia reflect hip strength as well as femoral DXA, but it remains to be tested whether such properties can be measured in vivo.

Published
2019
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17. Cross-linguistic patterns in the acquisition of quantifiers

Author

Torkildsen von Koss, Janne, Katsos, Napoleon, Cummins, Chris, Ezeizabarrena, Maria-Jose, Gavarro, Anna, Kuvac Kraljevic, Jelena, Hrzica, Gordana, Grohmann, Kleanthes K., Skordi, Athina, Jensen de Lopez, Kristine, Sundahl, Lone, Van Hout, Angeliek, Hollebrandse, Bart, Overweg, Jessica, Faber, Myrthe, Van Koert, Margreet, Smith, Nafsika, Vija, Maigi, Zupping, Sirli, Kunnari, Sari, Morisseau, Tiffany, Rusieshvili, Manana, Yatsushiro, Kazuko, Fengler, Anja, Varlokosta, Spyridoula, Konstantzou, Katerina, Farby, Shira, Guasti, Maria Teresa, Vernice, Mirta, Okabe, Reiko, Isobe, Miwa, Crosthwaite, Peter, Hong, Yoonjee, Balciuniene, Ingrida, Ahmad Nizar, Yanti Marina, Grech, Helen, Gatt, Daniela, Cheong, Win Nee, Asbjornsen, Arve, Haman, Ewa, Miekisz, Aneta, Gagarina, Natalia, Puzanova, Julia, Anđelković, Darinka, Savic, Maja, Josic, Smiljana, Slancova, Daniela, Kapalkova, Svetlana, Barberan, Tania, Ozge, Duygu, Hassan, Saima, Chan, Cecilia Yuet, Okubo, Tomoya, Van der Lely, Heather, Sauerland, Uli, Noveck, Ira, Özge, Duygu, Katsos, Napoleon, Cummins, Chris, Ezeizabarrena, Maria-Jose, Gavarro, Anna, Kraljevic, Jelena Kuvac, Hrzica, Gordana, Grohmann, Kleanthes K., Skordi, Athina, de Lopez, Kristine Jensen, Sundahl, Lone, van Hout, Angeliek, Hollebrandse, Bart, Overweg, Jessica, Faber, Myrthe, van Koert, Margreet, Smith, Nafsika, Vija, Maigi, Zupping, Sirli, Kunnari, Sari, Morisseau, Tiffany, Rusieshvili, Manana, Yatsushiro, Kazuko, Fengler, Anja, Varlokosta, Spyridoula, Konstantzou, Katerina, Farby, Shira, Guasti, Maria Teresa, Vernice, Mirta, Okabe, Reiko, Isobe, Miwa, Crosthwaite, Peter, Hong, Yoonjee, Balciuniene, Ingrida, Nizar, Yanti Marina Ahmad, Grech, Helen, Gatt, Daniela, Cheong, Win Nee, Asbjornsen, Arve, Torkildsen, Janne von Koss, Haman, Ewa, Miekisz, Aneta, Gagarina, Natalia, Puzanova, Julia, Andelkovic, Darinka, Savic, Maja, Josic, Smiljana, Slancova, Daniela, Kapalkova, Svetlana, Barberan, Tania, Hassan, Saima, Chan, Cecilia Yuet Hung, Okubo, Tomoya, van der Lely, Heather, Sauerland, Uli, Noveck, Ira, College of Social Sciences and Humanities, Department of Psychology, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Institute for Solid State Physics [Tokyo] (ISSP), The University of Tokyo (UTokyo), China National Research Center of Intelligent Equipment for Agriculture [Beijing] (NRCIEA), University of Oklahoma (OU), Kobe University, Institut des Sciences Cognitives (ISC), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institut des Sciences cognitives Marc Jeannerod - Laboratoire sur le langage, le cerveau et la cognition (L2C2), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), ACLC (FGw), Neurolinguistics and Language Development (NLD), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Grohmann, Kleanthes K. [0000-0003-4298-3191], Katsos, Napoleon [0000-0002-4722-674X], Ezeizabarrena, Maria-José [0000-0002-9108-9498], Apollo - University of Cambridge Repository, Katsosa, N, Cumminsb, C, Ezeizabarrenac, M, Gavarród, A, Kraljeviće, J, Hrzicae, G, Grohmannf, K, Skordig, A, De Lópezh, K, Sundahlh, L, Van Hout, A, Hollebrandse, B, Overweg, J, Faber, M, Van Koert, M, Smith, N, Vija, M, Zupping, S, Kunnari, S, Morisseau, T, Rusieshvili, M, Yatsushiro, K, Fengler, A, Varlokosta, S, Konstantzou, K, Farby, S, Guasti, M, Vernice, M, Okabev, R, Isobew, M, Crosthwaitex, P, Hongy, Y, Balčiunienez, I, Nizarg, Y, Grechaa, H, Gattaa, D, Cheongbb, W, Asbjørnsencc, A, Von Torkildsendd, J, Hamanee, E, Miȩkiszee, A, Gagarinaq, N, Puzanovaff, J, Andelković, D, Savić, M, Jošić, S, Slaňcováhh, D, Kapalkováii, S, Barberánc, T, Özgejj, D, Hassankk, S, Chanll, C, Okubomm, T, Van Der Lelynn, H, Sauerlandq, U, and Noveck, I

Subjects
Male, Computer science, Social Sciences, CHILDREN, MEANINGS, Generalization, Psychological, [SCCO]Cognitive science, Cognition, TURN-TAKING, Language acquisition, Quantifiers, Semantics, Pragmatics, Typology, Child, semantics, ComputingMilieux_MISCELLANEOUS, Language, media_common, Multidisciplinary, quantifiers, 4. Education, 05 social sciences, 06 humanities and the arts, Linguistics, language acquisition, Child, Preschool, 0602 languages and literature, Quantifier, Developmental linguistics, Language acquisition -- Case studies, Female, Comprehension, pragmatics, Universal, Cross linguistic, Pragmatic, Adult, Order of acquisition, media_common.quotation_subject, Generalization, Multidisciplinary sciences, 050105 experimental psychology, Perceptual Development, universals, Perception, LOGIC, Humans, Learning, 0501 psychology and cognitive sciences, Preschool, Competence (human resources), 060201 languages & linguistics, NUMBER WORDS, Grammar, Comparative and general -- Quantifiers, ADULTS, PERFORMANCE, Language Acquisition, Universals, Psychological, Semantic, UNIVERSALS
Abstract

This research was funded by European Cooperation in Science and Technology Action A33 “Cross-Linguistically Robust Stages of Children’s Linguistic Performance.” In addition, N.K., C.C., and I.N. were supported by the European Science Foundation Euro-XPrag Network; N.K., C.C., and N.S. were supported by the United Kingdom Economic and Social Research Council XPrag-UK Network; N.K. was supported by United Kingdom British Academy Grant SG090676; A.G. was supported by Spanish Ministerio de Economía y Competitividad Project FFI2014-56968-C4-1; A.G. and K.K.G. were supported by University of Cyprus Project 8037-61017; K.J.d.L. and L.S. were supported by Danish Council for Independent Research (Humanities) Grant 09-063957; M. Vija and S.Z. were supported by Estonian Science Foundation Grant ETF7492 and Estonian Research Council Grant SF0180056s08; K.Y. and U.S. were supported by European Commission for Education and Culture Grant 135295-LLP-2007-UK-KA1SCR and German Federal Ministry of Education and Research Grant 01UG0711; A.A. and J.v.K.T. were supported by a grant from the L. Meltzers Høyskolefond; E.H. and A.M. were supported by Grant 809/N-COST/2010/0 from the Polish Ministry of Science and Higher Education and National Science Centre; and D.A., M.S., and S.J. were supported by Grant ON179033 (2011-2014) from the Serbian Ministry of Education, Science, and Technological Development., Learners of most languages are faced with the task of acquiring words to talk about number and quantity. Much is known about the order of acquisition of number words as well as the cognitive and perceptual systems and cultural practices that shape it. Substantially less is known about the acquisition of quantifiers. Here we consider the extent to which systems and practices that support number word acquisition can be applied to quantifier acquisition and conclude that the two domains are largely distinct in this respect. Consequently, we hypothesize that the acquisition of quantifiers is constrained by a set of factors related to each quantifier’s specific meaning. We investigate competence with the expressions for ‘all’, ‘none’, ‘some’, ‘some ... not’ and ‘most’ in 31 languages, representing 11 language types, by testing 768 5-year-old children and 536 adults. We found a cross-linguistically similar order of acquisition of quantifiers, explicable in terms of four factors relating to their meaning and use. In addition, exploratory analyses reveal that language- and learner-specific factors, such as negative concord and gender, are significant predictors of variation., peer-reviewed

Published
2016
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18. Stiffness reconstruction methods for MR elastography

Author

Ralph, Sinkus, Fovargue, Daniel, Nordsletten, David, Sinkus, Ralph, Laboratoire de Recherche Vasculaire Translationnelle (LVTS (UMR_S_1148 / U1148)), Université Paris Diderot - Paris 7 (UPD7)-Université Paris 13 (UP13)-Institut National de la Santé et de la Recherche Médicale (INSERM), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Physique des ondes pour la médecine, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université Paris 13 (UP13)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV]Life Sciences [q-bio], [SDV.IB]Life Sciences [q-bio]/Bioengineering, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018
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19. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: applied to GENFI study

Author

John C. van Swieten, Maria Carmela Tartaglia, Sandro Sorbi, Marc Modat, Elizabeth Finger, Fabrizio Tagliavini, Mario Masellis, Stanley Durrleman, David M. Cash, Alexandre de Mendonça, James B. Rowe, Martina Bocchetta, Sebastien Ourselin, Jonathan D. Rohrer, Barbara Borroni, Robert Laforce, Jennifer M. Nicholas, Claire Cury, Daniela Galimberti, Giovanni B. Frisoni, Marco Lorenzi, Caroline Graff, Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Dementia Research Centre [London] (DRC), University College of London [London] (UCL), Department of Medical Physics and Biomedical Engineering (UCL), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), London School of Hygiene and Tropical Medicine (LSHTM), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Università degli Studi di Brescia = University of Brescia (UniBs), Centro Dino Ferrari [Milano], Università degli Studi di Milano = University of Milan (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Sunnybrook Research Institute [Toronto] (SRI), Sunnybrook Health Sciences Centre, Tanz Center Research in Neurodegenerative Diseases [Toronto], University of Toronto, Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge [UK] (CAM), Karolinska Institutet [Stockholm], Karolinska University Hospital [Stockholm], Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Neuroimaging and Telemedicine (LENITEM), IRCCS Fatebenefratelli - Brescia, Université Laval [Québec] (ULaval), University of Western Ontario (UWO), Faculdade de Medicina [Lisboa], Universidade de Lisboa = University of Lisbon (ULISBOA), Università degli Studi di Firenze = University of Florence (UniFI), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Brescia [Brescia], Università degli Studi di Milano [Milano] (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Universidade de Lisboa (ULISBOA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Thalamus, 02 engineering and technology, Disease, Genfi, 03 medical and health sciences, 0302 clinical medicine, Atrophy, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Medicine, Shape Analysis, business.industry, Spatiotemporal Analysis, Morphometry, [SCCO.NEUR]Cognitive science/Neuroscience, medicine.disease, Spatio temporal analysis, Frontal lobe, [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], Frontotemporal Dementia, Cohort, 020201 artificial intelligence & image processing, business, Neuroscience, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, LDDMM, Shape analysis (digital geometry), Frontotemporal dementia
Abstract

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer’s disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure.In this paper we propose a spatiotemporal analysis pipeline based Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects.We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

Published
2018

20. Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

Author

T. A. Callister, N. Kijbunchoo, G. Kang, C. C. Yancey, C. A. Costa, A. Dasgupta, M. C. Milovich-Goff, I. Belahcene, Jade Powell, V. Mangano, P. Schale, J. L. Wright, P. T.H. Pang, Francesco Pannarale, R. Flaminio, J.-P. Coulon, Yi-Ming Hu, C. Aulbert, A. Rocchi, A. E. Pace, Sumanta Tewari, C. Cocchieri, Alejandro Torres-Forné, B. Goncharov, R. Birney, Lloyd Paul Aiello, B. C. Pant, M. Afrough, R. A. Mercer, F. Jiménez-Forteza, Sourav Ghosh, Imre Bartos, W. H. Wang, Odylio D. Aguiar, J. E. Lord, E. J. Daw, Maurizio Canepa, M. Punturo, J. Neilson, Alex B. Nielsen, V. Quetschke, G. M. Guidi, G. Kuehn, S. Privitera, Antonio Marquina, Paolo Addesso, K. D. Giardina, D. Tuyenbayev, Maria Ilaria Del Principe, M. Agathos, Martin M. Fejer, J. R. Smith, S. Ghonge, K. AultONeal, C. S. Unnikrishnan, W. Katzman, R. Douglas, F. J. Raab, V. Kondrashov, N. Mukund, L. R. Cominsky, J. K. Wofford, M. A. Page, S. Khan, A. Lazzarini, C. Lazzaro, T. Z. Summerscales, V. Boschi, R. J. G. Jonker, Jay D. Tasson, M. Landry, L. Sammut, M. Factourovich, J. S. Lange, Patricia Schmidt, A. Avila-Alvarez, V. Brisson, J. C. Driggers, L. Di Fiore, B. Mours, C. Austin, A. Schönbeck, B. Rajbhandari, Daniel Wysocki, M. Barsuglia, Yuri Levin, G. Greco, L. van der Schaaf, S. A. Pai, D. Moraru, Antoine Heidmann, D. Töyrä, R. Gustafson, G. Rutins, P. J. Sutton, Graham Woan, Robert L. Byer, Stuart Reid, G. Valdes, A. Samajdar, Steven D. Penn, Jacob Scheuer, Gianpietro Cagnoli, Giuseppe Intini, Hyang Woon Lee, Robert Stone, D. Steinmeyer, E. G. Thomas, A. Basti, Ryan N. Lang, Jens Birch, E. A. Chase, D. Tao, P. A. Altin, Alvin J. K. Chua, D. Barker, B. Sassolas, Tristan Briant, L. Sun, H. Fong, O. Sauter, E. J. King, M. Muratore, Heinz-Bernd Eggenstein, Z. Du, P. Aufmuth, R. Bhandare, M. Gosselin, A. Allocca, Xing-Jiang Zhu, S. Antier, M. Dovale Álvarez, S. E. Strigin, M. Kasprzack, Michael L. Gorodetsky, J.-P. Zendri, Bernard F. Schutz, Li Ju, Daniel A. Shaddock, David Keitel, J. H. Romie, M. Drago, J. Meidam, Guenakh Mitselmakher, L. Wallace, Z. Frei, S. J. Cooper, J.-D. Fournier, S. Dhurandhar, Samuele Cortese, K. K.Y. Ng, A. Pal-Singh, Jong H. Chow, J. Liu, D. M. Macleod, Charalampos Markakis, S. T. Countryman, K. A. Thorne, Ryan Lynch, Brittany Kamai, Y. Bouffanais, Y. Q. Ma, B. A. Boom, L. Cunningham, A. Lartaux-Vollard, Suvadeep Bose, Vuk Mandic, R. A. Eisenstein, B. J. J. Slagmolen, R. Gouaty, Steven Bloemen, F. Frasconi, Jonathan R. Gair, Roman Schnabel, A. Lenon, B. R. Hall, C. Horst, G. D. Meadors, J. E. Brau, A. A. van Veggel, Andrew Matas, A. Pasqualetti, Surabhi Sachdev, Christopher J. Moore, J. Zweizig, K. Napier, Guido Mueller, Hang Yu, S. Doravari, Hsiao-Wen Chen, Maher Yazback, M. Pedraza, Patrick Brady, J. S. Areeda, T. Theeg, H. S. Cho, Efim A. Khazanov, G. Ballardin, Moritz Mehmet, S. J. Kapadia, J. M. Gonzalez Castro, R. Quitzow-James, R. M. S. Schofield, G. Cerretani, S. Biscoveanu, R. Sturani, Innocenzo M. Pinto, B. R. Iyer, M. Zanolin, Lee McCuller, Serena Vinciguerra, N. Mazumder, R. Pedurand, Benno Willke, P. Ehrens, J. Hennig, O. J. Piccinni, Antoni Ramos-Buades, A. Singhal, T. D. Abbott, O. Puncken, Zoheyr Doctor, A. W. Heptonstall, V. Gayathri, L. M. Perri, H. A. G. Gabbard, Alberto Vecchio, F. Martelli, T. Zelenova, M. Bawaj, Riccardo Bassiri, Magnus Manske, Will M. Farr, M. Montani, P. Weßels, A. Królak, Rana X. Adhikari, B. Pang, Simon Stevenson, Nancy Aggarwal, M. Vardaro, J. Ming, S. Koley, N. A. Lockerbie, S. S. Eikenberry, E. A. Muñiz, S. Brunett, B. Machenschalk, S. Walsh, F. Donovan, F. Y. Khalili, D. Sellers, E. Bonilla, G. Bergmann, L. Rei, Sebastian Steinlechner, B. Shapiro, Kazuhiro Agatsuma, Archisman Ghosh, E. L. Merilh, R. Kennedy, S. D. Linker, A. Bozzi, Stanislav Babak, M. Tonelli, A. Bisht, S. R. Morriss, Chunglee Kim, Miriam Cabero, E. Coughlin, Alessandra Buonanno, T. Hardwick, M. H. Wimmer, A. Effler, R. Metzdorff, H. Yamamoto, Gabrielle Allen, D. Barta, Soumi De, J.-M. Isac, J. S. Kissel, H. Cao, D. J. Vine, Duncan A. Brown, Tania Regimbau, W. Parker, I. Dave, M. Fletcher, B. Hughey, S. B. Anderson, A. Bohe, Geoffrey Lovelace, P. Hopkins, K. Izumi, B. D. Cheeseboro, C. Graef, Mark A. Scheel, M. Bejger, R. Kirchhoff, B. L. Pearlstone, David B. Tanner, S. C. McGuire, P. G. Murray, Hartmut Grote, T. P. Downes, N. Letendre, Roger Jones, T. Denker, G. Losurdo, A. Noack, V. J. Roma, S. J. Kimbrell, P. T. Baker, F. Travasso, A. P. Spencer, Kendall Ackley, R. K. Nayak, Lei Zhang, C. Whittle, S. S. Forsyth, J. A. Clark, A. S. Markosyan, G. Traylor, D. D. Brown, M. Fyffe, David Coward, Anthony A. Amato, N. van Bakel, Zachariah B. Etienne, P. Oppermann, M. J. Yap, Jennifer Watchi, R. Mittleman, P. Fulda, F. Marion, C. F. Da Silva Costa, Andrew Lundgren, S. Xiao, Nelson Christensen, V. Kringel, F. Carbognani, S. A. Usman, Carlos O. Lousto, C. Kent, J. Trinastic, A. Gennai, H. Y. Chia, M. Haney, L. Naticchioni, Piotr Jaranowski, R. Bonnand, D. Talukder, A. Masserot, M. Weinert, Salvatore Vitale, K. Kawabe, M. Constancio, A. R. Williamson, T. Sadecki, W. S. Kim, S. E. Barclay, D. Lumaca, Sascha Husa, K. E. Ramirez, A. S. Sengupta, S. M. Aston, K. Ueno, Michael W. Coughlin, A. L. Urban, M. Laxen, G. Venugopalan, D.B. DeBra, A. Giazotto, M. Tápai, P. Shawhan, M. Ducrot, I. Ferrante, Z. Khan, D. S. Rabeling, M. Di Giovanni, K. E. Gushwa, Ofek Birnholtz, K. Mason, J. Suresh, D. Hoak, M. F. Carney, R. Passaquieti, Neil J. Cornish, D. C. Coyne, V. Sandberg, R. Cavalieri, F. Cavalier, J. Eichholz, B. Weaver, D. Moffa, A. K. W. Chung, Erik Katsavounidis, R. De Pietri, Jocelyn Read, G. P. Newton, Gabriela Gonzalez, S. Gaudio, N. A. Robertson, D. Sentenac, J. A. Sonnenberg, Eric Howell, V. Germain, S. Gomes, R. De Rosa, K. R. Corley, J. M. Newport, S. Mastrogiovanni, R. K. Lanza, Sylvia J. Zhu, Samuel Deléglise, S. C. Tait, Rocco Romano, Rajesh Kumar, A. Fernandez-Galiana, Zifan Zhou, I. A. Bilenko, David J. Ottaway, P. Leaci, A. Colla, S. Leavey, Hai-Ping Cheng, M. Katolik, S. Ossokine, Jinsook Kim, L. K. Nuttall, Xin Chen, R. M. Martin, David A. Nichols, D. Nolting, C. North, Thomas Dent, C. Pankow, B. U. Gadre, Sebastien Biscans, D. H. Reitze, M. S. Shahriar, P. Ruggi, C. V. Kalaghatgi, M. J. Cowart, Michael Zevin, P. Dupej, Sumit Kumar, David Jonathan Hofman, B. Gateley, P. Corban, M. C. Tringali, I. Nardecchia, X. Guo, H. Vahlbruch, K. Riles, Vladimir Dergachev, R. Macas, P. M. Meyers, Barry C. Barish, G. Billingsley, S. Macfoy, L. Trozzo, Priscilla Canizares, H. Middleton, B. B. Miller, Howard Pan, Aaron Buikema, J. Hanson, Isabel Cordero-Carrión, Paul J. Groot, Samaya Nissanke, F. Fidecaro, Ashutosh Kumar Singh, M. Vasúth, S. Appert, L. Pinard, A. Cirone, Xavier Siemens, C. I. Torrie, Jan Harms, C. Mishra, B. Lantz, M. Favata, John Veitch, J. Lehmann, J. C. Barayoga, James Whelan, Jonathan Blackman, A. Moggi, C. Messick, J. Calderón Bustillo, A. Sergeev, H. N. Isa, M. Leonardi, P. Ajith, Carlos Cepeda, K. Toland, Linqing Wen, S. Grunewald, M. Chan, G. Vedovato, H. Qi, A. Viceré, A. Gopakumar, N. Arnaud, Maggie Tse, Vijay Varma, C. Fee, D. Meacher, M. MacInnis, S. Bae, P. Rapagnani, J. Hanks, E. Coccia, L. Kleybolte, Colm Talbot, Andreas Freise, Vincenzo Pierro, Paul D. Lasky, G. Stratta, Torrey Cullen, K. Siellez, L.-W. Wei, Bernard F. Whiting, Tobias Westphal, S. J. Chamberlin, Riccardo Ciolfi, Yi Chen, Athol J. Kemball, B. W. Schulte, S. Márka, Jesper Munch, Matthew Pitkin, R. K.L. Lo, José A. Font, L. McNeill, Maya Fishbach, A. F. Brooks, Satyanarayan Ray Pitambar Mohapatra, P. J. Veitch, R. M. Blair, Irene Fiori, K. Venkateswara, G. A. Prodi, Benjamin J. Owen, M. De Laurentis, R. P. Fisher, S. Mitra, C. Messenger, X. Fan, David Jones, E. A. Quintero, M. Granata, J. Devenson, K. S. Karvinen, Marco Cavaglia, J. F. J. van den Brand, Subroto Mukherjee, A. Markowitz, P. Charlton, R. Frey, K. W. K. Wong, P. Brockill, N. Leroy, Shiuh Chao, M. Nery, S. Sunil, M. Tacca, Otto A. Hannuksela, Martin Hendry, Cecilio García-Quirós, Christopher Wipf, M. C. Díaz, K. V. Tokmakov, M. Yvert, E. C. Ferreira, Aniello Grado, B. Bécsy, Kenneth A. Strain, M. Sieniawska, M. Kinley-Hanlon, Pablo Cerdá-Durán, Peter R. Saulson, J. Prasad, T. Prestegard, Gregory Ashton, Terry G. McRae, Antonios Kontos, Fabio Marchesoni, P. B. Covas, Kyungmin Kim, Debarati Chatterjee, B. O'Reilly, P. Gruning, G. Pillant, Junwei Cao, J. Warner, L. G. Prokhorov, Olivier Minazzoli, M. Razzano, J. L. Willis, A. Nitz, V. Tiwari, M. J. Szczepańczyk, Fabien Kéfélian, E. Schreiber, F. Robinet, A. Post, Seog Oh, J. H. Hough, P. W. F. Forsyth, G. McIntyre, A. Cumming, F. Di Renzo, G. Mendell, F. Baldaccini, G. Dálya, Karan Jani, E. Goetz, E. Capocasa, Huang-Wei Pan, Gijs Nelemans, Leo Singer, L. F. Ortega, M. J. Hart, F. Nocera, I. Magaña Hernandez, E. D. Hall, T. J. Massinger, Jolien D. E. Creighton, T. Vo, C. De Rossi, Ryan Magee, A. Gupta, T. Shaffer, V. Raymond, J. K. Blackburn, S. Raja, J. W. W. Pratt, E. Chassande-Mottin, M. Korobko, Gregory M. Harry, V. Kalogera, A. K. Zadrożny, Sheila Rowan, M. Pürrer, S. Kwang, S. E. Dwyer, A. Sawadsky, Todd Adams, G. D. Hammond, Harald P. Pfeiffer, D. M. Wilken, M. Fitz-Axen, P. Bacon, Daniel E. Holz, G. Ciani, A. Parida, E. J. Son, A. Sheperd, M. van Beuzekom, M. Rizzo, H. K. Lee, V. Predoi, I. W. Martin, H. J. Bulten, David Cohen, David E. McClelland, B. Patricelli, D. S. Wu, Margaret Millhouse, Rosa Poggiani, L. Glover, S. G. Gaonkar, Haixing Miao, V. Frey, T. Di Girolamo, Karl Gill, S. Farinon, A. Pele, J. J. Bero, O. Bock, G. L. Mansell, K. L. Dooley, S. L. Danilishin, Lisa Barsotti, Arunava Mukherjee, M. Fays, R. Bork, Jing Wang, Y. Minenkov, A. Di Lieto, M. Saleem, C. Vorvick, Nicholas Demos, J. D. Lough, S. Kaufer, Jonathan Cripe, C. Beer, László Á. Gergely, Daichi Tsuna, Kevin M. Ryan, F. Vetrano, Rainer Weiss, F. Ricci, E. A. Houston, J. Westerweck, Y.-M. Kim, K. Mogushi, D. Estevez, Z. Tornasi, T. Etzel, P. J. King, K. H. Lai, Aaron Viets, J. Feicht, Joey Shapiro Key, J. R. Sanders, S. B. Coughlin, J. Betzwieser, John Miller, S. Caride, J. C. Bayley, H. Wittel, S. G. Schwalbe, D. Buskulic, W. W. Johnson, M. Poe, M. Phelps, R. G. Ormiston, C. Affeldt, D. Ugolini, C. Cahillane, R. Taylor, B. Day, A. Corsi, R. T. DeRosa, David Blair, M. Oliver, A. J. Weinstein, E. Cesarini, V. Dolique, A. Chiummo, E. Cuoco, N. Bode, Luca Gammaitoni, J. A. Giaime, B. K. Berger, F. Ohme, P. Clearwater, M. A. Papa, A. S. Bell, M. K. M. Bader, E. J. Fauchon-Jones, A. Brillet, T. Huynh-Dinh, M. Wade, D. J. Stops, P. Kumar, Christopher M. Biwer, S. Frasca, C. Rajan, Kai Staats, S. Meshkov, R. DeSalvo, Stephen Fairhurst, Carl Blair, C. C. Buchanan, A. Hreibi, O. V. Palashov, Sean T. McWilliams, F. Piergiovanni, Chang-Hwan Lee, M. Davier, M. Was, R. Robie, M. M. Hanke, Jordan Camp, P. Popolizio, Koji Arai, K. Lee, Edward K. Porter, H. Vocca, Christopher P. L. Berry, A. D. Silva, A. Grant, Eugeniy E. Mikhailov, A. A. Shah, M. Merzougui, L. Matone, A. Staley, Lionel London, S. Jawahar, V. Loriette, L. Conti, A. L. Stuver, Matthew Evans, T. D. Creighton, J. G. Rollins, E. J. Sanchez, Steven Reyes, J. McIver, Archana Pai, F. Paoletti, Laura Sampson, James G. Bartlett, T. J. N. Nelson, D. Schuette, D. H. Shoemaker, J. Junker, James Healy, Roy Williams, Mairi Sakellariadou, Richard O'Shaughnessy, B. Barr, A. Rüdiger, O. de Varona, Leo Tsukada, W. Z. Korth, E. Z. Hamilton, Tenglin Li, C. Buy, G. D. O'Dea, I. Kowalska, H. Overmier, Swetha Bhagwat, J. Scott, A. Ain, E. Maros, W. G. Anderson, G. Cella, Richard J. Abbott, Marie-Anne Bizouard, B. P. Abbott, E. K. Gustafson, R. Coyne, Fabrice Matichard, E. K. Wessel, Nergis Mavalvala, Jerome Degallaix, C. Casentini, Ben Farr, Christophe Collette, Ho-Gyu Lee, C. J. Perez, N. Gehrels, C.-J. Haster, O. Halim, J. A. Taylor, K. W. Tsang, M. Rakhmanov, Ik Siong Heng, T. Souradeep, E. Mejuto-Villa, J. Page, M. Boer, A. L. Miller, Mingliang Zhou, P. Astone, Larne Pekowsky, S. D'Antonio, H. Fehrmann, W. Ren, Benjamin William Allen, Mandar Patil, Abhirup Ghosh, Karsten Danzmann, Hua Wang, Slawomir Gras, C. Krämer, Denis Martynov, Marco Bazzan, A. Kutynia, F. Salemi, Federico Ferrini, Lijing Shao, G. Vajente, S. M. Gaebel, P. Raffai, M. C. Edwards, Angela Delli Paoli, S. Ascenzi, D. Mukherjee, Alessandro Bertolini, Miftar Ganija, Laleh Sadeghian, R. C. Walet, V. P. Mitrofanov, D. Huet, S. H. Huttner, M. C. Araya, F. Cleva, Andrew Melatos, M. Lorenzini, W. Del Pozzo, P. F. Cohadon, H. Heitmann, Mark Hannam, R. L. McCarthy, C. R. Billman, Tanja Hinderer, A. Strunk, R. L. Savage, M. P. Ross, Maximiliano Isi, V. Sequino, Lionel Martellini, E. M. Gretarsson, Peter Fritschel, D. J. McManus, M. Masso-Reid, Yu Zhang, L. E. Sanchez, J. R. Palamos, Enrico Calloni, Thomas Corbitt, L. Salconi, Jacob Broida, M. K. Gupta, D. V. Atallah, Laura Cadonati, A. Perreca, Daniel R. George, S. McCormick, Susan M. Scott, I. Khan, Y. Wang, Q. Chu, G. Wang, C. E. Cirelli, L. E. Wade, Chad Hanna, M. Pirello, A. K. Srivastava, T. B. Edo, Haocun Yu, F. Clara, S. V. Angelova, L. Magaña Zertuche, Lili Yang, M. Branchesi, S. Chua, Fiodor Sorrentino, J. C. Batch, E. Massera, R. Inta, Alicia M. Sintes, Edward Seidel, M. Ast, T. Zhang, Thibaut Jacqmin, J. Woehler, M. Prijatelj, E. M. Fries, S. Klimenko, D. Passuello, D. B. Kozak, Douglas Davis, Geraint Pratten, H. Fair, G. Bogaert, Andrea Taracchini, B. Agarwal, Eric Thrane, J. B. Kanner, Walter Winkler, Chunnong Zhao, C. Michel, D. Finstad, Harald Lück, S. Rieger, L. Nevin, L. Kuo, D. Verkindt, Sarah Caudill, T. D. Knowles, M. Cho, H. Radkins, V. B. Adya, G. Gaur, S. Di Pace, E. Genin, J. V. van Heijningen, I. Maksimovic, R. L. Ward, G. Hemming, M. Pichot, C. Palomba, S. M. Koehlenbeck, A. Ananyeva, S. Karki, S. P. Vyatchanin, V. Dattilo, M. Brinkmann, Z. Márka, Stefan Hild, J. Schmidt, P. Couvares, R. Tso, Patrick M. Koch, S. Banagiri, F. Garufi, Surendra Nadh Somala, D. Bersanetti, Tomasz Bulik, D. Fiorucci, Richard J. Oram, V. V. Frolov, François Bondu, Britton D. Smith, J. Oberling, A. C. Green, Fausto Acernese, Tyson Littenberg, Mi Zhang, C. Gray, G. H. Ogin, M. Mantovani, G. Gemme, D. Pascucci, C. Bradaschia, K. Bossie, S. Vass, T. Dal Canton, Leopoldo Milano, S. G. Crowder, L. Rolland, N. Indik, Fabrizio Barone, M. Mohan, Mark Thomas, N. Man, J. Worden, David A. Williams, I. W. Harry, C. M. Mow-Lowry, K. Haughian, A. R. Wade, S. Chung, D. C. Vander-Hyde, E. E. Cowan, J. D. Romano, J. Casanueva Diaz, C. Van Den Broeck, Nicolas Sanchis-Gual, Michael E Zucker, B. D. Lackey, B. L. Swinkels, Kipp Cannon, M. Steinke, A. Mullavey, F. Magaña-Sandoval, P. Puppo, A. W. Muir, E. A. Huerta, M. C. Heintze, R. Goetz, C. L. Romel, M. Lormand, M. A. Okada, P. Thomas, Shubhanshu Tiwari, Ettore Majorana, Paul M. Ricker, M. Bitossi, K. Wette, F. Thies, John J. Oh, Timothy Evans, Rory Smith, C. Adams, D. Sigg, S. W. Ballmer, D. M. Shoemaker, Michelle E. Walker, J. George, G. Moreno, D. Rosińska, S. E. Gossan, Patrice Hello, K. Holt, Kevin Barkett, I. Dorrington, I. Di Palma, Collin D. Capano, M. B. Shaner, V. Fafone, Jessica Steinlechner, A. Neunzert, T. Chmiel, M. Heurs, C. Dreissigacker, J-Y. Vinet, Reed Essick, S. Kandhasamy, B. Sorazu, T. T. Nguyen, Andrea Chincarini, Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux (ARTEMIS), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China (IMP), Institut de Chimie de la Matière Condensée de Bordeaux (ICMCB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), The Open University [Milton Keynes] (OU), State Key Laboratory of Marine Geology [Shanghai], Tongji University, Institut des Hautes Etudes Scientifiques (IHES), IHES, University of Malaya, MEMS CORE Co, Laboratoire d'Annecy de Physique des Particules (LAPP/Laboratoire d'Annecy-le-Vieux de Physique des Particules), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Laboratoire des matériaux avancés (LMA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Center for Neuroendocrinology, University of Otago [Dunedin, Nouvelle-Zélande], Laboratoire de l'Accélérateur Linéaire (LAL), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Albert Einstein Institute, AstroParticule et Cosmologie (APC (UMR_7164)), Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Department of Physics, Columbia University [New York], Université Libre de Bruxelles [Bruxelles] (ULB), Department of Environmental Science and Analytical Chemistry [Stockholm] (ACES), Stockholm University, Association de Coordination Technique Agricole (ACTA), Department of Astrophysics [Nijmegen], Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radboud university [Nijmegen]-Radboud university [Nijmegen], LAboratoire de REcherche en Géodésie [Paris] (LAREG), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Observatoire de Haute-Provence (OHP), Institut Pythéas (OSU PYTHEAS), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Department of Chemistry, University of Alberta, University of Alberta, Physics Department, University of Massachusetts System (UMASS), Département de Physique, Universita degli studi di Genova, Department of Chemical Engineering, Facultad de Ciencias Quimicas, CSSAR, Chinese Academy of Sciences [Beijing] (CAS), Department of Chemistry [West Bengal], Kalyani University, UMR CNRS 8179, Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS), Department of Physics and Astronomy [Northfield], Carleton College, Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Cités, Territoires, Environnement et Sociétés (CITERES), Université de Tours-Centre National de la Recherche Scientifique (CNRS), European Organization for Nuclear Research (CERN), Department of Physiology and Pharmacology, Università degli Studi di Roma 'La Sapienza' [Rome]-Réseau International des Instituts Pasteur (RIIP)-Institut Pasteur, Fondation Cenci Bolognetti - Istituto Pasteur Italia, Fondazione Cenci Bolognetti, Réseau International des Instituts Pasteur (RIIP), LIGO Laboratory, California Institute of Technology (CALTECH), Vitrociset S.p.a., Department of Physics [Parma], University of Parma, Albert-Einstein-Institut, Max-Planck-Institut, Department of Mathematics [Berkeley], University of California [Berkeley], University of California-University of California, Istituto Nazionale di Fisica Nucleare, Sezione di Genova (INFN, Sezione di Genova), National Institute for Nuclear Physics (INFN), Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona] (ICM), Consejo Superior de Investigaciones Científicas [Spain] (CSIC), Dipartimento di Fisica, Università 'La Sapienza', Università degli Studi di Roma 'La Sapienza' [Rome], Institut de Mécanique des Fluides et des Solides (IMFS), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire des Matériaux Inorganiques (LMI), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Clermont Université-Sigma CLERMONT (Sigma CLERMONT)-Centre National de la Recherche Scientifique (CNRS), Ecosystèmes forestiers (UR EFNO), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Polymer-Institut, University of Karlsruhe, University of Karlsruhe (TH), Los Gatos Res Inc, Los, Paul-Drude-Institut für Festkörperelektronik, Istituto Nazionale di Fisica Nucleare, Sezione di Firenze (INFN, Sezione di Firenze), Laboratoire Kastler Brossel (LKB (Lhomond)), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), University of Minnesota, University of Minnesota [Twin Cities], University of Minnesota System-University of Minnesota System, Virgo, Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), JIFRESSE, University of California [Los Angeles] (UCLA), Civil and Environmental Engineering (CEE), University of Illinois at Urbana-Champaign [Urbana], University of Illinois System-University of Illinois System, Bhabha Atomic Research Center (BARC), Government of India, Department of Atomic Energy, ArcelorMittal Maizières Research SA, ArcelorMittal, Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), ESPCI ParisTech, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Istituto di Fisica dello Spazio Interplanetario (IFSI), Consiglio Nazionale delle Ricerche (CNR), Ecosystèmes méditerranéens et risques (UR EMAX), School of Environmental Sciences, University of Ulster, Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institute of Climate System, Chinese Academy of Meteorological Sciences (CAMS), Forestry School, University of Extremadura, Plasencia, Universidad de Extremadura (UEX), Photonics and photonic materials group, Departement of Physics, University of bath, University of Bath [Bath], Laboratoire de Mesures et Modélisation de la Migration des Radionucléides (L3MR), Service d'Etudes du Comportement des Radionucléides (SECR), Département de Physico-Chimie (DPC), CEA-Direction de l'Energie Nucléaire (CEA-DEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction de l'Energie Nucléaire (CEA-DEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Département de Physico-Chimie (DPC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Océan du Large et Variabilité Climatique (OLVAC), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Department of Biomedical Sciences, Universita degli Studi di Padova, Istituto Nazionale di Fisica Nucleare, Sezione di Catania (INFN), Università degli studi di Catania [Catania], Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Istituto IMEM-CNR, Istituto dei Materiali per l'Elettronica ed il Magnetismo - Consiglio Nazionale delle Ricerche [Genova] (IMEM-CNR), Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Department of Physics Department [Fayetteville], University of Arkansas [Fayetteville], Politecnico di Torino [Torino] (Polito), SensorDynamics AG, Université Nice Sophia Antipolis (UNS), State Key Lab Marine Geology, Laboratoire de physique et chimie des nano-objets (LPCNO), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UPS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), University of Otago, AstroParticule et Cosmologie (APC - UMR 7164), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Association de Coordination Technique Agricole, LAboratoire de REcherche en Géodésie [Paris] (LAREG/IGN), Institut National de l'Information Géographique et Forestière [IGN] (IGN), University of Massachusetts, Laboratoire des Fluides Complexes et leurs Réservoirs (LFCR), TOTAL FINA ELF-Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), University of Minnesota [Minneapolis], University of California at Los Angeles [Los Angeles] (UCLA), Istituto Nazionale di Astrofisica (INAF), Universidad de Extremadura, Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), OLVAC LEGOS, Université Toulouse III - Paul Sabatier (UPS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Etudes Spatiales (CNES)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UPS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Etudes Spatiales (CNES)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP)-Centre National de la Recherche Scientifique (CNRS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Institut des Hautes Études Scientifiques (IHES), University of Malaya = Universiti Malaya [Kuala Lumpur, Malaisie] (UM), Laboratoire d'Annecy de Physique des Particules (LAPP), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Max Planck Institute for Gravitational Physics (Albert Einstein Institute) (AEI), Max-Planck-Gesellschaft, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université libre de Bruxelles (ULB), Les instituts techniques agricoles (Acta), Radboud University [Nijmegen]-Radboud University [Nijmegen], Università degli studi di Genova = University of Genoa (UniGe), The University of Western Australia (UWA), Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur, Fondation Cenci Bolognetti - Istituto Pasteur Italia, Fondazione Cenci Bolognetti, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli studi di Parma = University of Parma (UNIPR), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Istituto Nazionale di Fisica Nucleare (INFN), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Clermont Université-SIGMA Clermont (SIGMA Clermont)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), European gravitational Observatory (EGO), Istituto Nazionale di Fisica Nucleare (INFN)-Centre National de la Recherche Scientifique (CNRS), Paul-Drude-Institut für Festkörperelektronik (PDI), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), University of Minnesota System, Civil and Environmental Engineering [Illinois] (CEE), Bhabha Atomic Research Centre (BARC), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Max-Planck-Institut für Gravitationsphysik ( Albert-Einstein-Institut ) (AEI), Universidad de Extremadura - University of Extremadura (UEX), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Padova = University of Padua (Unipd), Università degli studi di Catania = University of Catania (Unict), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Istituto dei Materiali per l'Elettronica ed il Magnetismo [Genova] (IMEM-CNR), Politecnico di Torino = Polytechnic of Turin (Polito), LIGO Scientific Collaboration, Virgo Collaboration, Centre National de la Recherche Scientifique (CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - 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Istituto Pasteur Italia, Fondazione Cenci Bolognetti, University of Parma = Università degli studi di Parma [Parme, Italie], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Université Pierre et Marie Curie - Paris 6 (UPMC)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7), The LIGO Scientific Collaboration, The Virgo Collaboration, (Astro)-Particles Physics, Abbott, B. P., Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R. X., Adya, V. B., Affeldt, C., Afrough, M., Agarwal, B., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, B., Allen, G., Allocca, A., Altin, P. A., Amato, A., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arnaud, N., Ascenzi, S., Ashton, G., Ast, M., Aston, S. M., Astone, P., Atallah, D. V., Aufmuth, P., Aulbert, C., Aultoneal, K., Austin, C., Avila-Alvarez, A., Babak, S., Bacon, P., Bader, M. K. M., Bae, S., Baker, P. T., Baldaccini, F., Ballardin, G., Ballmer, S. W., Banagiri, S., Barayoga, J. C., Barclay, S. E., Barish, B. C., Barker, D., Barkett, K., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Batch, J. C., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Beer, C., Bejger, M., Belahcene, I., Bell, A. S., Berger, B. K., Bergmann, G., Bero, J. J., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhagwat, S., Bhandare, R., Bilenko, I. A., Billingsley, G., Billman, C. R., Birch, J., Birney, R., Birnholtz, O., Biscans, S., Biscoveanu, S., Bisht, A., Bitossi, M., Biwer, C., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bloemen, S., Bock, O., Bode, N., Boer, M., Bogaert, G., Bohe, A., Bondu, F., Bonilla, E., Bonnand, R., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossie, K., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Branchesi, M., Brau, J. E., Briant, T., Brillet, A., Brinkmann, M., Brisson, V., Brockill, P., Broida, J. E., Brooks, A. F., Brown, D. A., Brown, D. D., Brunett, S., Buchanan, C. C., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Buy, C., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Calderón Bustillo, J., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Canizares, P., Cannon, K. C., Cao, H., Cao, J., Capano, C. D., Capocasa, E., Carbognani, F., Caride, S., Carney, M. F., Diaz, J. Casanueva, Casentini, C., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cepeda, C. B., Cerdá-Durán, P., Cerretani, G., Cesarini, E., Chamberlin, S. J., Chan, M., Chao, S., Charlton, P., Chase, E., Chassande-Mottin, E., Chatterjee, D., Cheeseboro, B. D., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Chia, H., Chincarini, A., Chiummo, A., Chmiel, T., Cho, H. S., Cho, M., Chow, J. H., Christensen, N., Chu, Q., Chua, A. J. K., Chua, S., Chung, A. K. W., Chung, S., Ciani, G., Ciolfi, R., Cirelli, C. E., Cirone, A., Clara, F., Clark, J. A., Clearwater, P., Cleva, F., Cocchieri, C., Coccia, E., Cohadon, P. -F., Cohen, D., Colla, A., Collette, C. G., Cominsky, L. R., Constancio, M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corley, K. R., Cornish, N., Corsi, A., Cortese, S., Costa, C. A., Coughlin, E., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Cowan, E. E., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Cripe, J., Crowder, S. G., Cullen, T. J., Cumming, A., Cunningham, L., Cuoco, E., Canton, T. Dal, Dálya, G., Danilishin, S. L., D'Antonio, S., Danzmann, K., Dasgupta, A., Da Silva Costa, C. F., Dattilo, V., Dave, I., Davier, M., Davis, D., Daw, E. J., Day, B., De, S., Debra, D., Degallaix, J., De Laurentis, M., Deléglise, S., Del Pozzo, W., Demos, N., Denker, T., Dent, T., De Pietri, R., Dergachev, V., De Rosa, Rosario, Derosa, R. T., De Rossi, C., Desalvo, R., De Varona, O., Devenson, J., Dhurandhar, S., Díaz, M. C., Di Fiore, L., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Di Pace, S., Di Palma, I., Di Renzo, F., Doctor, Z., Dolique, V., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Douglas, R., Dovale Álvarez, M., Downes, T. P., Drago, M., Dreissigacker, C., Driggers, J. C., Du, Z., Ducrot, M., Dupej, P., Dwyer, S. E., Edo, T. B., Edwards, M. C., Effler, A., Eggenstein, H. -B., Ehrens, P., Eichholz, J., Eikenberry, S. S., Eisenstein, R. A., Essick, R. C., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Factourovich, M., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fee, C., Fehrmann, H., Feicht, J., Fejer, M. M., Fernandez-Galiana, A., Ferrante, I., Ferreira, E. C., Ferrini, F., Fidecaro, F., Finstad, D., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fitz-Axen, M., Flaminio, R., Fletcher, M., Fong, H., Font, J. A., Forsyth, P. W. F., Forsyth, S. S., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fries, E. M., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Gabbard, H., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gammaitoni, L., Ganija, M. R., Gaonkar, S. G., Garcia-Quiros, C., Garufi, F., Gateley, B., Gaudio, S., Gaur, G., Gayathri, V., Gehrels, N., Gemme, G., Genin, E., Gennai, A., George, D., George, J., Gergely, L., Germain, V., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giaime, J. A., Giardina, K. D., Giazotto, A., Gill, K., Glover, L., Goetz, E., Goetz, R., Gomes, S., Goncharov, B., González, G., Gonzalez Castro, J. M., Gopakumar, A., Gorodetsky, M. L., Gossan, S. E., Gosselin, M., Gouaty, R., Grado, A., Graef, C., Granata, M., Grant, A., Gras, S., Gray, C., Greco, G., Green, A. C., Gretarsson, E. M., Groot, P., Grote, H., Grunewald, S., Gruning, P., Guidi, G. M., Guo, X., Gupta, A., Gupta, M. K., Gushwa, K. E., Gustafson, E. K., Gustafson, R., Halim, O., Hall, B. R., Hall, E. D., Hamilton, E. Z., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanson, J., Hardwick, T., Harms, J., Harry, G. M., Harry, I. W., Hart, M. J., Haster, C. -J., Haughian, K., Healy, J., Heidmann, A., Heintze, M. C., Heitmann, H., Hello, P., Hemming, G., Hendry, M., Heng, I. S., Hennig, J., Heptonstall, A. W., Heurs, M., Hild, S., Hinderer, T., Hoak, D., Hofman, D., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Houston, E. A., Howell, E. J., Hreibi, A., Hu, Y. M., Huerta, E. A., Huet, D., Hughey, B., Husa, S., Huttner, S. H., Huynh-Dinh, T., Indik, N., Inta, R., Intini, G., Isa, H. N., Isac, J. -M., Isi, M., Iyer, B. R., Izumi, K., Jacqmin, T., Jani, K., Jaranowski, P., Jawahar, S., Jiménez-Forteza, F., Johnson, W. W., Jones, D. I., Jones, R., Jonker, R. J. G., Ju, L., Junker, J., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Karki, S., Karvinen, K. S., Kasprzack, M., Katolik, M., Katsavounidis, E., Katzman, W., Kaufer, S., Kawabe, K., Kéfélian, F., Keitel, D., Kemball, A. J., Kennedy, R., Kent, C., Key, J. S., Khalili, F. Y., Khan, I., Khan, S., Khan, Z., Khazanov, E. A., Kijbunchoo, N., Kim, Chunglee, Kim, J. C., Kim, K., Kim, W., Kim, W. S., Kim, Y. -M., Kimbrell, S. J., King, E. J., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Koch, P., Koehlenbeck, S. M., Koley, S., Kondrashov, V., Kontos, A., Korobko, M., Korth, W. Z., Kowalska, I., Kozak, D. B., Krämer, C., Kringel, V., Królak, A., Kuehn, G., Kumar, P., Kumar, R., Kumar, S., Kuo, L., Kutynia, A., Kwang, S., Lackey, B. D., Lai, K. H., Landry, M., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lee, C. H., Lee, H. K., Lee, H. M., Lee, H. W., Lee, K., Lehmann, J., Lenon, A., Leonardi, M., Leroy, N., Letendre, N., Levin, Y., Li, T. G. F., Linker, S. D., Littenberg, T. B., Liu, J., Lo, R. K. L., Lockerbie, N. A., London, L. T., Lord, J. E., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lousto, C. O., Lovelace, G., Lück, H., Lumaca, D., Lundgren, A. P., Lynch, R., Ma, Y., Macas, R., Macfoy, S., Machenschalk, B., Macinnis, M., Macleod, D. M., Magaña Hernandez, I., Magaña-Sandoval, F., Magaña Zertuche, L., Magee, R. M., Majorana, E., Maksimovic, I., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Marchesoni, F., Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Martelli, F., Martellini, L., Martin, I. W., Martin, R. M., Martynov, D. V., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Matichard, F., Matone, L., Mavalvala, N., Mazumder, N., Mccarthy, R., Mcclelland, D. E., Mccormick, S., Mcculler, L., Mcguire, S. C., Mcintyre, G., Mciver, J., Mcmanus, D. J., Mcneill, L., Mcrae, T., Mcwilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Meidam, J., Mejuto-Villa, E., Melatos, A., Mendell, G., Mercer, R. A., Merilh, E. L., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Miao, H., Michel, C., Middleton, H., Mikhailov, E. E., Milano, L., Miller, A. L., Miller, B. B., Miller, J., Millhouse, M., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Ming, J., Mishra, C., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Moffa, D., Moggi, A., Mogushi, K., Mohan, M., Mohapatra, S. R. P., Montani, M., Moore, C. J., Moraru, D., Moreno, G., Morriss, S. R., Mours, B., Mow-Lowry, C. M., Mueller, G., Muir, A. W., Mukherjee, Arunava, Mukherjee, D., Mukherjee, S., Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Muratore, M., Murray, P. G., Napier, K., Nardecchia, I., Naticchioni, L., Nayak, R. K., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Nevin, L., Newport, J. M., Newton, G., Ng, K. K. Y., Nguyen, T. T., Nichols, D., Nielsen, A. B., Nissanke, S., Nitz, A., Noack, A., Nocera, F., Nolting, D., North, C., Nuttall, L. K., Oberling, J., O'Dea, G. D., Ogin, G. H., Oh, J. J., Oh, S. H., Ohme, F., Okada, M. A., Oliver, M., Oppermann, P., Oram, Richard J., O'Reilly, B., Ormiston, R., Ortega, L. F., O'Shaughnessy, R., Ossokine, S., Ottaway, D. J., Overmier, H., Owen, B. J., Pace, A. E., Page, J., Page, M. A., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pal-Singh, A., Pan, Howard, Pan, Huang-Wei, Pang, B., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Papa, M. A., Parida, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patil, M., Patricelli, B., Pearlstone, B. L., Pedraza, M., Pedurand, R., Pekowsky, L., Pele, A., Penn, S., Perez, C. J., Perreca, A., Perri, L. M., Pfeiffer, H. P., Phelps, M., Piccinni, O. J., Pichot, M., Piergiovanni, F., Pierro, V., Pillant, G., Pinard, L., Pinto, I. M., Pirello, M., Pitkin, M., Poe, M., Poggiani, R., Popolizio, P., Porter, E. K., Post, A., Powell, J., Prasad, J., Pratt, J. W. W., Pratten, G., Predoi, V., Prestegard, T., Prijatelj, M., Principe, M., Privitera, S., Prodi, G. A., Prokhorov, L. G., Puncken, O., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Quetschke, V., Quintero, E. A., Quitzow-James, R., Raab, F. J., Rabeling, D. S., Radkins, H., Raffai, P., Raja, S., Rajan, C., Rajbhandari, B., Rakhmanov, M., Ramirez, K. E., Ramos-Buades, A., Rapagnani, P., Raymond, V., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Ren, W., Reyes, S. D., Ricci, F., Ricker, P. M., Rieger, S., Riles, K., Rizzo, M., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rolland, L., Rollins, J. G., Roma, V. J., Romano, J. D., Romano, R., Romel, C. L., Romie, J. H., Rosińska, D., Ross, M. P., Rowan, S., Rüdiger, A., Ruggi, P., Rutins, G., Ryan, K., Sachdev, S., Sadecki, T., Sadeghian, L., Sakellariadou, M., Salconi, L., Saleem, M., Salemi, F., Samajdar, A., Sammut, L., Sampson, L. M., Sanchez, E. J., Sanchez, L. E., Sanchis-Gual, N., Sandberg, V., Sanders, J. R., Sassolas, B., Saulson, P. R., Sauter, O., Savage, R. L., Sawadsky, A., Schale, P., Scheel, M., Scheuer, J., Schmidt, J., Schmidt, P., Schnabel, R., Schofield, R. M. S., Schönbeck, A., Schreiber, E., Schuette, D., Schulte, B. W., Schutz, B. F., Schwalbe, S. G., Scott, J., Scott, S. M., Seidel, E., Sellers, D., Sengupta, A. S., Sentenac, D., Sequino, V., Sergeev, A., Shaddock, D. A., Shaffer, T. J., Shah, A. A., Shahriar, M. S., Shaner, M. B., Shao, L., Shapiro, B., Shawhan, P., Sheperd, A., Shoemaker, D. H., Shoemaker, D. M., Siellez, K., Siemens, X., Sieniawska, M., Sigg, D., Silva, A. D., Singer, L. P., Singh, A., Singhal, A., Sintes, A. M., Slagmolen, B. J. J., Smith, B., Smith, J. R., Smith, R. J. E., Somala, S., Son, E. J., Sonnenberg, J. A., Sorazu, B., Sorrentino, F., Souradeep, T., Spencer, A. P., Srivastava, A. K., Staats, K., Staley, A., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Stevenson, S. P., Stone, R., Stops, D. J., Strain, K. A., Stratta, G., Strigin, S. E., Strunk, A., Sturani, R., Stuver, A. L., Summerscales, T. Z., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Swinkels, B. L., Szczepańczyk, M. J., Tacca, M., Tait, S. C., Talbot, C., Talukder, D., Tanner, D. B., Tao, D., Tápai, M., Taracchini, A., Tasson, J. D., Taylor, J. A., Taylor, R., Tewari, S. V., Theeg, T., Thies, F., Thomas, E. G., Thomas, M., Thomas, P., Thorne, K. A., Thrane, E., Tiwari, S., Tiwari, V., Tokmakov, K. V., Toland, K., Tonelli, M., Tornasi, Z., Torres-Forné, A., Torrie, C. I., Töyrä, D., Travasso, F., Traylor, G., Trinastic, J., Tringali, M. C., Trozzo, L., Tsang, K. W., Tse, M., Tso, R., Tsukada, L., Tsuna, D., Tuyenbayev, D., Ueno, K., Ugolini, D., Unnikrishnan, C. S., Urban, A. L., Usman, S. A., Vahlbruch, H., Vajente, G., Valdes, G., Van Bakel, N., Van Beuzekom, M., Van Den Brand, J. F. J., Van Den Broeck, C., Vander-Hyde, D. C., Van Der Schaaf, L., Van Heijningen, J. V., Van Veggel, A. A., Vardaro, M., Varma, V., Vass, S., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venkateswara, K., Venugopalan, G., Verkindt, D., Vetrano, F., Viceré, A., Viets, A. D., Vinciguerra, S., Vine, D. J., Vinet, J. -Y., Vitale, S., Vo, T., Vocca, H., Vorvick, C., Vyatchanin, S. P., Wade, A. R., Wade, L. E., Wade, M., Walet, R., Walker, M., Wallace, L., Walsh, S., Wang, G., Wang, H., Wang, J. Z., Wang, W. H., Wang, Y. F., Ward, R. L., Warner, J., Was, M., Watchi, J., Weaver, B., Wei, L. -W., Weinert, M., Weinstein, A. J., Weiss, R., Wen, L., Wessel, E. K., Weßels, P., Westerweck, J., Westphal, T., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wilken, D., Williams, D., Williams, R. D., Williamson, A. R., Willis, J. L., Willke, B., Wimmer, M. H., Winkler, W., Wipf, C. C., Wittel, H., Woan, G., Woehler, J., Wofford, J., Wong, K. W. K., Worden, J., Wright, J. L., Wu, D. S., Wysocki, D. M., Xiao, S., Yamamoto, H., Yancey, C. C., Yang, L., Yap, M. J., Yazback, M., Yu, Hang, Yu, Haocun, Yvert, M., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, L., Zhang, M., Zhang, T., Zhang, Y. -H., Zhao, C., Zhou, M., Zhou, Z., Zhu, S. J., Zhu, X. J., Zucker, M. E., Zweizig, J., COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), Centre National de la Recherche Scientifique (CNRS)-Université de Tours, and Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome]

Subjects
Astronomy, Testing, detection, General Physics and Astronomy, EFFICIENT, TESTING RELATIVISTIC GRAVITY, Tensors, Spectral shapes, 01 natural sciences, General Relativity and Quantum Cosmology, Gravitational wave background, Energy density, TOOL, QC, ComputingMilieux_MISCELLANEOUS, stochastic model, Mathematical physics, QB, Physics, [PHYS]Physics [physics], Stochastic systems, Gravitational effects, article, Vectors, Polarization (waves), gravitational waves, astro-ph.CO, [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity, Cosmology and Nongalactic Astrophysics (astro-ph.CO), General relativity, gr-qc, FOS: Physical sciences, experimental studies of gravity, General Relativity and Quantum Cosmology (gr-qc), Gravity waves, Relativity, Reference frequency, Physics and Astronomy (all), Theory of relativity, Scalar modes, Tests of general relativity, 0103 physical sciences, Advanced LIGO, ddc:530, Tensor, 010306 general physics, STFC, Gravitational Waves, polarization, 010308 nuclear & particles physics, Gravitational wave, RCUK, Astrophysical sources, LIGO, Physics and Astronomy, gravitation, RADIATION, Stochastic Background, Dewey Decimal Classification::500 | Naturwissenschaften::530 | Physik, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], cosmology, Gravitational Waves, Stochastic Background, Advanced LIGO
Abstract

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually-unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically-polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy-densities of tensor, vector, and scalar modes at 95% credibility to $\Omega^T_0 < 5.6 \times 10^{-8}$, $\Omega^V_0 < 6.4\times 10^{-8}$, and $\Omega^S_0 < 1.1\times 10^{-7}$ at a reference frequency $f_0 = 25$ Hz., Comment: Minor updates to match published version

Published
2018
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21. The importance of the pericardium for cardiac biomechanics: From physiology to computational modeling

Author

Martin R. Pfaller, Radomir Chabiniok, Wolfgang A. Wall, Andreas Nagler, Julia M. Hörmann, Cristóbal Bertoglio, Martina Weigl, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Université Paris-Saclay, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Bernoulli Institute for Mathematics for Mathematics, Computer Science and Artificial Intelligence Groningen, University of Groningen [Groningen], École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, and Computational and Numerical Mathematics

Subjects
FOS: Computer and information sciences, 02 engineering and technology, 030204 cardiovascular system & hematology, Computational Engineering, Finance, and Science (cs.CE), Cardiac mechanical modeling, 0302 clinical medicine, Pericardium, PASSIVE MYOCARDIUM, Computer Science - Computational Engineering, Finance, and Science, SOLE MECHANISM, Models, Cardiovascular, Pericardial cavity, Mechanics, MECHANICAL-PROPERTIES, VENTRICULAR MECHANICS, Magnetic Resonance Imaging, Biomechanical Phenomena, medicine.anatomical_structure, HEARTH, Modeling and Simulation, Heart Function Tests, cardiovascular system, Finite element simulation, Biotechnology, Materials science, Systole, Finite Element Analysis, 0206 medical engineering, ATRIOVENTRICULAR PLANE DISPLACEMENT, FOS: Physical sciences, PRESSURE, Dashpot, 03 medical and health sciences, Imaging, Three-Dimensional, Approximation error, ATRIAL, medicine, Humans, Computer Simulation, Boundary value problem, [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], Boundary conditions, Plane (geometry), Mechanical Engineering, QUANTIFICATION, 020601 biomedical engineering, Physics - Medical Physics, Medical Physics (physics.med-ph), Stress, Mechanical, ORIENTATION, Displacement (fluid), Normal
Abstract

International audience; The human heart is enclosed in the pericardial cavity. The pericardium consists of a layered thin sac and is separated from the myocardium by a thin film of fluid. It provides a fixture in space and friction-less sliding of the myocardium. The influence of the pericardium is essential for predictive mechanical simulations of the heart. However, there is no consensus on physiologically correct and computationally tractable pericardial boundary conditions. Here we propose to model the pericardial influence as a parallel spring and dashpot acting in normal direction to the epicardium. Using a four-chamber geometry, we compare a model with pericardial boundary conditions to a model with fixated apex. The influence of pericardial stiffness is demonstrated in a parametric study. Comparing simu-lation results to measurements from cine magnetic resonance imaging reveals that adding pericardial boundary conditions yields a better approximation with respectto atrioventricular plane displacement, atrial filling, and overall spatial approximation error. We demonstrate that this simple model of pericardial-myocardial interaction can correctly predict the pumping mechanisms ofthe heart as previously assessed in clinical studies. Utilizing a pericardial model can not only provide much more realistic cardiac mechanics simulations but also allows new insights into pericardial-myocardial interaction which cannot be assessed in clinical measurement.

Published
2018

22. An adaptive Hybridizable Discontinuous Galerkin approach for cardiac electrophysiology

Author

Hoermann, Julia M., Bertoglio, Cristóbal, Kronbichler, Martin, Pfaller, Martin R., Chabiniok, Radomir, Wall, Wolfgang A., Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), University of Groningen [Groningen], Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Université Paris-Saclay, Institute for Computational Mechanics, Technical University of Munich, Garching, Germany, École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
finite element method, Hybridizable Discontinuous Galerkin, [SDV.IB]Life Sciences [q-bio]/Bioengineering, cardiac electrophysiology, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

International audience; Cardiac electrophysiology simulations are numerically challenging due to the propagation of a steep electrochemical wave front and thus require discretizations with small mesh sizes to obtain accurate results. In this work, we present an approach based on the Hybridizable Discontinuous Galerkin method (HDG), which allows an efficient implementation of high-order discretizations into a computational framework. In particular using the advantage of the discontinuous function space, we present an efficient p-adaptive strategy for accurately tracking the wave front. HDG allows to reduce the overall degrees of freedom in the final linear system to those only on the element interfaces. Additionally, we propose a rule for a suitable integration accuracy for the ionic current term depending on the polynomial order and the cell model to handle high-order polynomials. Our results show that for the same number of degrees of freedom coarse high-order elements provide more accurate results than fine low-order elements. Introducing p-adaptivity further reduces computational costs while maintaining accuracy by restricting the use of high-order elements to resolve the wave front. For a patient-specific simulation of a cardiac cycle p-adaptivity reduces the average number of degrees of freedom by 95% compared to the non-adaptive model. In addition to reducing computational costs, using coarse meshes with our p-adaptive high-order HDG method also simplifies practical aspects of mesh generation and postprocessing.

Published
2018
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23. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013

Author

Yong Zhao, Hadi Danawi, Bach Xuan Tran, Gene Bukhman, Vasiliki Stathopoulou, Taavi Tillmann, Nelson Alvis-Guzman, Yongmei Li, Jerry Puthenpurakal Abraham, Sudan Prasad Neupane, Jack Caravanos, Ben Schöttker, Rafael Lozano, Damian G Hoy, Yoshihiro Kokubo, Nicholas J K Breitborde, Sergey Soshnikov, Yukito Shinohara, Randall V. Martin, Michael Brainin, Fernando Perez-Ruiz, Yingfeng Zheng, Santosh Mishra, Julio Cesar Montañez Hernandez, Michael Phillips, Belinda J. Gabbe, Hebe N. Gouda, Ziad A. Memish, Rupert R A Bourne, Guoqing Hu, Emmanuel A. Ameh, Abigail McLain, Michelle L. Bell, Christopher Margono, Marissa Iannarone, Wilkister N. Moturi, Donald H. Silberberg, Carl Abelardo T. Antonio, Nataliya Foigt, Anand Dayama, Yanping Wang, Amanda J. Mason-Jones, Bolajoko O. Olusanya, Monica S. Vavilala, Katherine B Gibney, David Tanne, Sidibe S Kany Roseline, Marcella Montico, Abhishek Singh, Sarah Derrett, Alireza Esteghamati, Seok Jun Yoon, Corine Karema, Rakhi Dandona, David M. Pereira, Kazem Rahimi, Gitanjali M. Singh, Vivekanand Jha, John Hornberger, Anne M. Riederer, Kathryn H. Jacobsen, Andrea Pedroza, Lily Alexander, Fiona M. Blyth, Tommi Vasankari, Kyle J Foreman, Rana J. Asghar, Tilahun Nigatu Haregu, Yousef Khader, Rafael Alfonso-Cristancho, Suzanne Barker-Collo, Lydia S. Atkins, Simerjot K. Jassal, Mohammad Ali Sahraian, Peter Scarborough, Hans W. Hoek, E. Ray Dorsey, Muluken Dessalegn, David C. Schwebel, Gavin Shaddick, Thomas D. Fleming, Mohammad Tavakkoli, Dorairaj Prabhakaran, Mohammad H. Forouzanfar, Christopher C. Mapoma, Jost B. Jonas, Erin C Mullany, Gene F. Kwan, Johanna M. Geleijnse, Antonio Luiz Pinho Ribeiro, Roberto Tchio Talongwa, Tolesa Bekele, Jed D. Blore, Gunn Marit Aasvang, Philimon Gona, Miguel Angel Alegretti, Babak Eshrati, Mitsuru Mukaigawara, Richard F. Gillum, Odgerel Chimed-Ochir, Ubai Alsharif, Richard C. Franklin, Felix Masiye, Richard T. Burnett, Sanjay Krishnaswami, Martin McKee, John J Huang, Lucía Cuevas-Nasu, Wagner Marcenes, Walid Ammar, Knud Juel, Joseph R. Zunt, Martha M Téllez Rojo, Mamta Swaroop, Noela M Prasad, Azmeraw T. Amare, Tim Driscoll, Michael Kravchenko, Heresh Amini, Amir Sapkota, Theo Vos, Charlotte Watts, Dennis Odai Laryea, D. Alex Quistberg, Justin Beardsley, Cheng Huang, Adnan M. Durrani, Sarah V Thackway, Rita Van Dingenen, Manami Inoue, Martha Híjar, Honglei Chen, Amany H. Refaat, Yichong Li, Vineet K. Chadha, Wenzhi Wang, Louisa Degenhardt, Kingsley N. Ukwaja, Nayu Ikeda, James D. Wilkinson, Linh N Bui, Maria Hagströmer, Gonghuan Yang, Ann Kristin Knudsen, David J. Margolis, Soewarta Kosen, Hans Kromhout, Atsushi Goto, Man Mohan Mehndiratta, Thomas N. Williams, Michael Soljak, Yun Jin Kim, Hideaki Toyoshima, Jeyaraj D Pandian, Borja del Pozo-Cruz, Soufiane Boufous, Ivy Shiue, Anders Larsson, Guilherme V. Polanczyk, John Powles, Yara A. Halasa, Robin Room, Ratilal Lalloo, Carolina Batis Ruvalcaba, Panniyammakal Jeemon, Elisabete Weiderpass, Jürgen Rehm, Ejaz Ahmad Khan, Alicia Aleman, Zacharie Tsala Dimbuene, Elena Alvarez, Rachelle Buchbinder, Randah R. Hamadeh, Bryan Hubbell, Sadaf G. Sepanlou, Farhad Islami, Costas A. Christophi, Heidi Stöckl, Ismael R. Campos-Nonato, Nigel Bruce, Edward J Mills, Samuel A L Perry, Taavi Lai, Baffour Awuah, Mete Saylan, Karen J. Courville, Arindam Basu, Vanessa De la Cruz-Góngora, Teresita González de Cosío, Naohiro Yonemoto, Frida Namnyak Ngalesoni, Muluken Azage Yenesew, Atte Meretoja, Michael Brauer, Cyrus Cooper, Giorgia Giussani, Valentina S. Arsic Arsenijevic, Vasiliy Victorovich Vlassov, André Karch, Leilei Duan, Matthew M Coates, Omid Ameli, Gelin Xu, Matthias Endres, Ganesan Karthikeyan, Ione Jayce Ceola Schneider, Mohamed Hsairi, Palwasha Anwari, Mazin J. Al Khabouri, Dariush Mozaffarian, Juan R. Sanabria, Pablo M. Lavados, Sumeet S. Chugh, Johan Ärnlöv, Ivo Rakovac, Maurice Giroud, Haidong Kan, Ibrahim Abdelmageem Mohamed Ginawi, José Luis Texcalac-Sangrador, Luigi Naldi, Erica Leigh Slepak, Deena Alasfoor, James E. Saunders, Richard Matzopoulos, Talal Bakfalouni, Stein Emil Vollset, Andrea Werdecker, Lennert J. Veerman, Lorenzo Monasta, Henrica A. F. M. Jansen, Reyna A Gutiérrez, Brittany Wurtz, Luz Maria Sanchez, Lijing L. Yan, M. Patrice Lindsay, Michele Meltzer, Sanjay Basu, Steven van de Vijver, Alaa Badawi, Thomas Claßen, Young-Ho Khang, Brett M. Kissela, Jun Zhu, In-Hwan Oh, Fiona J Charlson, Maria Cecilia Bahit, Dinorah González-Castell, Rosario Cárdenas, Dan Poenaru, Sayed Saidul Alam, Mitchell T. Wallin, Harish Chander Gugnani, James Leigh, Ferrán Catalá-López, Lidia Morawska, Jim van Os, Stephanie J. London, Kaire Innos, Isabelle Romieu, Fiorella Cavalleri, Adrian Davis, Hwee Pin Phua, Chakib Nejjari, Héctor Gómez Dantés, Boris I. Pavlin, Karen Sliwa, Lynne Gaffikin, Constance D. Pond, Michael F. MacIntyre, Blake Thomson, Norberto Perico, Ronny Westerman, Samantha M. Colquhoun, Michael H. Criqui, Ana Maria Nogales Vasconcelos, Wubegzier Mekonnen, Bulat Idrisov, Ana Basto-Abreu, Andrew G. M. Bulloch, Jasvinder A. Singh, Vinod K. Paul, Emin Murat Tuzcu, Svetlana Popova, Hmwe H Kyu, Richard L. Guerrant, Mohammed I. Albittar, Srikanth Mangalam, Steven E. Lipshultz, Lela Sturua, Semaw Ferede Abera, Eduardo Bernabé, George D. Thurston, Bruno F. Sunguya, Tiffany Ku, Alejandra G. Contreras, Abdullah Sulieman Terkawi, Charles Atkinson, Ashkan Afshin, Heidi J. Larson, Abdullatif Husseini, Jose C. Adsuar, Reza Assadi, Ademola Lukman Adelekan, Joshua A. Salomon, Yousef M. Elshrek, Gokalp Kadri Yentur, Devina Nand, Narayanaswamy Venketasubramanian, Graça Maria Ferreira De Lima, Maheswar Satpathy, Fotis Topouzis, Traolach S. Brugha, Hywel C Williams, Coen H. Van Gool, Andrew H. Kemp, Awoke Misganaw, Amado D Quezada, Norito Kawakami, Bert Brunekreef, Peter Burney, Tati S. Warouw, Jongmin Lee, Inga Dora Sigfusdottir, Marcel Tanner, Solveig A. Cunningham, Benjamin O. Anderson, Tariku Jibat Beyene, Lars Barregard, Xia Wan, Giuseppe Remuzzi, Bernadette Thomas, Lilia S Pedraza, Massimo Cirillo, Alina Rodriguez, Ricky Leung, Farshad Pourmalek, K. Srinath Reddy, Charles D.A. Wolfe, Ulrich O Mueller, Neeraj Bedi, Al Artaman, Lucia Hernandez, Itamar S. Santos, C. Arden Pope, Norlinah Mohamed Ibrahim, Carlo Irwin A. Panelo, Selen Begüm Uzun, Miltiadis K. Tsilimbaris, Anwar Rafay, Daniel Dicker, Melvin Barrientos Marzan, Sajjad Ur Rahman, Mohammed O. Basulaiman, Edgar P. Simard, Mohammad T Mashal, Maysaa El Sayed Zaki, Shiwei Liu, Don C. Des Jarlais, Bo Norrving, Salvador Villalpando, Miia Kivipelto, Yang Liu, Carolina Maria Teixeira, Catalina Medina, Sudha Jayaraman, Josep Maria Haro, Diego De Leo, Angel J Paternina Caicedo, Abigail C. McKay, Eric L. Ding, Mukesh Dherani, Ljiljana Pejin Stokic, Vinay Nangia, Sukanta Saha, Juan Liang, Elisabeth Cardis, Zourkaleini Younoussi, José R Nogueira, Braden Te Ao, Vasco Manuel Pedro Machado, Lionel Racapé, Ting Wu Chuang, Shahab Khatibzadeh, E Filipa de Castro, Barthelemy Kuate Defo, Ulises Trujillo, Alan D. Lopez, Soraya Seedat, Lope H Barrero, Linhong Wang, Daniel Pope, Alexandra Brazinova, Faris Lami, Valentina Colistro, G Anil Kumar, Derek F J Fay, Haidong Wang, Hwashin H. Shin, Raimundas Lunevicius, Suzanne Polinder, Dietrich Plass, David Rojas-Rueda, Stephen S Lim, Tania G Sánchez-Pimienta, K.M. Venkat Narayan, Yuantao Hao, Jung-Chen Chang, Corina Benjet, Seyed-Mohammad Fereshtehnejad, Luciano A. Sposato, Stan Biryukov, Kunihiro Matsushita, Beth E. Ebel, Cleusa P. Ferri, Soumya Swaminathan, K. Ryan Wessells, Gustavo Velasquez-Melendez, Leslie T. Cooper, David O. Carpenter, Nancy Lopez, Bryan L. Sykes, Sandra Nolte, Murray B. Stein, Paul N. Jensen, Fabiola Mejía-Rodríguez, Xiaonong Zou, Bradford D. Gessner, Dhruv S. Kazi, Dragos Virgil Davitoiu, Alejandra Jáuregui, Pouria Heydarpour, Megan Bohensky, Harvey Whiteford, Berrak Bora Basara, Zhengming Chen, Gregory R. Wagner, Paul I. Dargan, Hermann Brenner, Nima Hafezi-Nejad, John Nelson Opio, Scott Weichenthal, Deborah Salvo, Jun She, Tea Lallukka, Carolyn C. Gotay, Stephen G. Waller, Christian Kieling, Shivanthi Balalla, Valery L. Feigin, Qing Lan, Matias Trillini, Adam D M Briggs, Sungroul Kim, Niveen M E Abu-Rmeileh, Renata Micha, Sergey Petrovich Ermakov, Ole Frithjof Norheim, Zulfiqar A Bhutta, Paul S. F. Yip, Grant Nguyen, Ralph L. Sacco, Biju Abraham, Ken Takahashi, Jixiang Ma, Peter A. Meaney, Ayse Abbasoglu Ozgoren, Kimberly Cooperrider, M Rifat Kose, Shams Eldin Ali Hassan Khalifa, Rasmus Havmoeller, Alize J. Ferrari, Kebede Deribe, Nadim E. Karam, George A. Mensah, Bongani M. Mayosi, Konrad Pesudovs, Joanna Moschandreas, Ziad Nahas, James Damsere-Derry, Nsanzimana Sabin, Tonatiuh Barrientos-Gutiérrez, Ying Jiang, Andre Pascal Kengne, Peter Allebeck, Jonas Minet Kinge, Shankuan Zhu, Guy B. Marks, Daniel C Casey, Marco A Avila, Anna Roca, Lalit Dandona, Ami R. Moore, Adansi A. Amankwaa, David Gunnell, Andre Keren, Yohannes Adama Melaku, Nhung T Nguyen, Anthony D. Woolf, Mayuree Rao, Peter J. Allen, Christina Papachristou, Karzan Abdulmuhsin Mohammad, Ravi Kumar Balu, Marie Ng, Marcello Tonelli, Maziar Moradi-Lakeh, Maigeng Zhou, Emmanuela Gakidou, Mohammed K. Ali, Amanda W Pain, Dan J. Stein, Kawkab Shishani, Fortuné Gbètoho Gankpé, Howard J. Hoffman, James Scott, Nadine Steckling, Samia Alhabib, Deborah Jarvis, Kara Estep, Arsène Kouablan Adou, Ricardo Orozco, Holly Hagan, K. C. Astha, Reza Malekzadeh, Klara Dokova, Aliya Naheed, Ernst J. Kuipers, Valeria Caso, Derrick A Bennett, Andrea B. Feigl, Uche S. Uchendu, Holly E. Erskine, Shireen Sindi, Arjun Lakshmana Balaji, Francesco Saverio Violante, Monika Sawhney, Alejandra Cantoral, Ketevan Goginashvili, Raghib Ali, Fan Jiang, Robert G. Weintraub, Homie Razavi, Myriam Tobollik, Howard Hu, Emerito Jose A. Faraon, Irma Khonelidze, Patricia M. Riccio, Eun-Kee Park, Julio Cesar Campuzano, Ibrahim Abubakar, Jürgen C Schmidt, Konstantinos Stroumpoulis, Aref A. Bin Abdulhak, Graeme J. Hankey, Natan M. Bornstein, Mouhanad Hammami, Lee Richardson, Rintaro Mori, Alanur Çavlin, Ruth W Kimokoti, Samir Soneji, Mark J. Nieuwenhuijsen, John Q. Wong, Joseph Frostad, Tom Achoki, Rahman Shiri, Ashish Bhalla, Kurt Straif, Simon I. Hay, Scott B. Patten, Kalpana Balakrishnan, Awoke Misganaw Temesgen, Chandrashekhar T Sreeramareddy, Ryan M Barber, Rosana E. Norman, JianLi Wang, Siyi Shangguan, Luke Nyakarahuka, Kovin Naidoo, Charles D. H. Parry, Mercedes Colomar, H. Ross Anderson, Carlos Magis-Rodriguez, Joan M. Nolla, Muhammad Imran Nisar, Karen Devries, Andrew L. Thorne-Lyman, Denis Nash, Marape Marape, Rajiv Chowdhury, Dima M. Qato, Luca Ronfani, Nobhojit Roy, Daniel Kim, Yuichiro Yano, Luke D. Knibbs, Margaret Robinson, Hilda L Harb, Rogelio Pérez-Padilla, Janet L Leasher, Jonathan L. Wright, Peter Brooks, Cristiana Abbafati, Belinda K Lloyd, Victor Aboyans, Nikhil Tandon, Charles R. Newton, Simón Barquera, Ted R. Miller, Kinnari S. Murthy, Habib Benzian, Glen Mola, Paulo A. Lotufo, Burcu Kucuk Bicer, Peggy Pei-Chia Chiang, Alexander Kraemer, Solomon Meseret Woldeyohannes, Saman Fahimi, Lesley Rushton, Kim Moesgaard Iburg, Vafa Rahimi-Movaghar, Logan Sandar, Bruce Neal, Teresa Shamah Levy, Karen M. Tabb, Jeffrey A. Towbin, Christopher J L Murray, Ramesh Sahathevan, Aaron Cohen, Chanda Kulkarni, Van C. Lansingh, François Alla, Tasara T. Mazorodze, Murugesan Raju, Saeid Shahraz, Uchechukwu K.A. Sampson, Rajeev Gupta, Neil Pearce, Mustafa Z. Younis, Veena S. Kulkarni, Francisco A García-Guerra, Amanda G. Thrift, Stefan Ma, Samaya Ismayilova, Evariste Gasana, Amitava Banerjee, Aslam Pervaiz, Emilie Agardh, Abraham D. Flaxman, Farshad Farzadfar, Peter W. Gething, Ileana Heredia-Pi, Boris Bikbov, Wanqing Chen, Saad B. Omer, Ruben Castro, Neeraj Bhala, Sara Sheikhbahaei, Hilton Lam, Urbano Fra Paleo, Lidia Sanchez-Riera, Alicia Elena Beatriz Lawrynowicz, Kristen Delwiche, Richard G. Ellenbogen, Max Petzold, Yuri Y Varakin, Guilherme Borges, Guohong Jiang, Francis Guillemin, Kyle R. Heuton, Yohannes Kinfu, Victoria F Bachman, Joseph A Wagner, Carlos A Castañeda-Orjuela, Leonardo Trasande, Abbas Ali Mahdi, Josef Coresh, Chuanhua Yu, Kenji Shibuya, Berrin Serdar, Laetitia Huiart, Xiaofeng Liang, Jean de Dieu Ngirabega, Takayoshi Ohkubo, Natalie Stephens, Francis Apolinary Mhimbira, Jefferson Traebert, Amiran Gamkrelidze, Kjetil Søreide, Samath D Dharmaratne, Robert P. Dellavalle, George Mugambage Ruhago, Lakshmi Vijayakumar, Joannie Lortet-Tieulent, Dipan Bose, Tania C Aburto, Saleem M Rana, Miriam Levi, Mohammad Taghi Hedayati, Rodolfo S Pagcatipunan, Ron T. Gansevoort, H. D. Hosgood, Michael Burch, Mohsen Naghavi, Vegard Skirbekk, Ayfer Pekericli, Walter Mendoza, Pengpeng Ye, Gabrielle deVeber, Ali H. Mokdad, David M. Broday, Koranteng Adofo, Zewdie Aderaw Alemu, Shifalika Goenka, Carrie Beth Peterson, Nicolas J. C. Stapelberg, Edson Serván-Mori, Anil Kaul, Foad Abd-Allah, Marek Majdan, Rahul Gupta, Giancarlo Logroscino, Kardiyoloji, Peterson, Carrie B., Laboratoire de Physique des Solides (LPS), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Wisconsin Division of Public Health, Laboratoire de psychologie sociale et de psychologie cognitive (LAPSCO), Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Erosion torrentielle neige et avalanches (UR ETGR (ETNA)), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), London South Bank University, Metropolitan Police Service, Hong Kong Baptist University (HKBU), Institut für Informatik [München/Munich] (LMU), Ludwig-Maximilians-Universität München (LMU), Neuroépidémiologie Tropicale (NET), Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Chirurgie Thoracique et Vasculaire - Médecine vasculaire [CHU Limoges], CHU Limoges, Insight Centre for Data Analytics [Galway] (INSIGHT), National University of Ireland [Galway] (NUI Galway), Maladies chroniques, santé perçue, et processus d'adaptation (APEMAC), Université Paris Descartes - Paris 5 (UPD5)-Université de Lorraine (UL), Centre d'Investigation Clinique - Epidemiologie Clinique/essais Cliniques Nancy, Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale (INSERM), Karolinska Institute, karolinska institute, Department of Molecular Biosciences, Department of Molecular Biosciences [Oslo], Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO)-Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO), Centro de Estudios Avanzados en Zonas Aridas (CEAZA), Ecole Polytechnique Fédérale de Lausanne (EPFL), Regional Genetic Service, St Mary's Hospital, Manchester, Laboratoire d'Ingénierie des Matériaux (LIM), Centre National de la Recherche Scientifique (CNRS), Computer Science Department [Bristol], University of Bristol [Bristol], Universität Mannheim [Mannheim], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) (SRI-B), Computational Science and Engineering Department [Daresbury] (STFC), Science & Technologie Facilities Council, Multimedia Research Center (MRC), University of Alberta, Division of Biostatistics (Biostat - MINNEAPOLIS), University of Minnesota [Twin Cities], University of Minnesota System-University of Minnesota System, University of Southampton, Imperial College London, Neurology Department, Ichilov Medical Center, Interactions, transferts, ruptures artistiques et culturels - EA 6301 (InTRu), Université de Tours, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Julius Center for Health Sciences and Primary Care, University Medical Center [Utrecht], Risk Assessment Sciences Institute, Utrecht University [Utrecht], Unité de Biologie Fonctionnelle et Adaptative (BFA (UMR_8251 / U1133)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Respiratory Epidemiology and Public Health, Imperial College London-Royal Brompton Hospital-National Heart and Lung Institute [UK], CIBER de Epidemiología y Salud Pública (CIBERESP), STAR laboratory, Stanford University [Stanford], Unité de recherche Virologie et Immunologie Moléculaires (VIM), Institut National de la Recherche Agronomique (INRA), Tennent Institute of Ophthalmology, National University of Singapore (NUS), Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Multidisciplinary Nanotechnology Centre, Swansea University, Cyprus International Institute for the Environment and Public Health, Harvard School of Public Health, Genomics Research Center, Academia Sinica, Neuro Rehab Services LLP [New Delhi], Department of Signal Theory and Communications (TSC), Univ. Politec. de Catalunya, King‘s College London, Dept. Mat. Engn. De Ma, Sao Carlos, Fed. Univ. Sao Carlos UFSCar, RESPEC (RESPEC), RESPEC, Advanced Laboratories on Embedded Systems [Roma] (ALES), Department of Biology [Miami], University of Miami [Coral Gables], Health Care, Minister Of Labour-Ministry of Labor, Health and Social Affairs, Department of Nephrology, University Medical Center, University of Groningen, Division of Human Nutrition, Wageningen University and Research Centre [Wageningen] (WUR), Spatial Ecology and Epidemiology Group, University of Oxford [Oxford], College of Medicine, University of Hail, Saudi Arabia, Laboratory of Neurologic Diseases, Mario Negri Institute, Milan, Department of Civil Engineering [Hamirpur], National Institute of Technology [Hamirpur], GEMMA — Environmental Engineering and Microbiology Research Group, Department of Hydraulic, Maritime and Environmental Engineering, Universitat Politècnica de Catalunya [Barcelona] (UPC), Department of Biosciences and Nutrition, Karolinska Institutet [Stockholm], Institut National de Recherche et d'Analyse Physico-Chimique (INRAP), Institut National de Recherche et d'Analyse Physico-chimique (Ariana, Tunisie) (INRAP), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Technologie de Belfort-Montbeliard (UTBM), Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences [Tehran] (SBUMS), Shahid Beheshti University-Shahid Beheshti University, Center for Applied Medical Research (CIMA), University of Tehran, Secretariat of the Pacific Community, Public Health Division, Sociétés, Acteurs, Gouvernement en Europe (SAGE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), College of Precision Instrument and Optoelectronics Engineering, Tianjin University, George Washington University (GW), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U912 INSERM - Aix Marseille Univ - IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universiteit Gent [Ghent], Washington State University (WSU), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, School of Computer Science - China University of Geosciences (China University of Geosciences (East Area)), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université Catholique de Louvain (UCL), Fachbereich Physik [Berlin], Freie Universität Berlin, Div Cyclotron & Radiopharmaceut Sci (DRDO, INMAS), Univ New Delhi, School of Physics and Astronomy, University of St Andrews [Scotland], University of Cape Town, Department of Neuroscience, Department of Computer Science and Engineering [Daejeon] (Chungnam National University), Lawrence University, Gastroenterology & Hepatology, Tata Research Development and Design Center (TRDDC), TCS Innovation Labs, University of Helsinki, Google Inc [Mountain View], Research at Google, Swedish Defense Research Agency (FOI), Servicio de Neurologia (SANTIAGO - Neurologie), Universidad del Desarrollo, Novartis Pharmaceutical Corporation, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble [1985-2015] (OSUG [1985-2015]), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Department of Mechanical and Automation Engineering (CAD Laboratory), The Chinese University of Hong Kong [Hong Kong], Università degli studi di Bari, Department of Health and Human Services, National Institutes of Health [Bethesda] (NIH), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), College of Information and Electrical Engineering [Beijing] (CIEE), China Agricultural University (CAU), Thales Research and Technology [Palaiseau], THALES, Department of Rheumatology and Connective Tissue Diseases, Medical University of Lublin, Barts and The London School of Medicine and Dentistry, Queen Mary University of London (QMUL), Centre d'économie de la Sorbonne (CES), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Paris School of Economics (PSE), École supérieure du professorat et de l'éducation - Académie de Grenoble [2013-2019] (ESPE Grenoble [2013-2019]), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Mathematics, University of Iowa [Iowa City], College of Medicine, Alfaisal University, Saudi Ministry of Health, Institut national des recherches agricoles du Bénin, Centre de Recherches agricoles du Sud, Departments of Epidemiology and Nutrition, Unit of Human Nutrition, Agricultural University of Athens, Department of Animal Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, University of Virginia, University of Virginia [Charlottesville], Epidemiology and Biostatistics Unit, Institute for Maternal and Child Health - IRCCS ‘‘Burlo Garofolo', Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Division of Cardiovascular Medicine and Channing Division of Network Medicine, Brigham and Women's Hospital [Boston], American University of Beirut [Beyrouth] (AUB), Department of Chemistry, Scientific Computing Research Unit, Department of dermatology, Milano University-Azienda Ospedaleria Ospedali Riuniti di Bergamo, Mailman School of Public Health, Columbia University [New York], The Georges Institute for International Health, The University of Sydney, Department of epidemiology and Public Health, Faculty of Medicine, Département Optique (OPT), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Universitat Pompeu Fabra [Barcelona], Center for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra [Barcelona]-Catalunya ministerio de salud, Nutriments Lipidiques et Prévention des Maladies Métaboliques, Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université de la Méditerranée - Aix-Marseille 2, Department of Neurology Lunds University Hospital Lund, Unit of Functional Bionanomaterials, School of Biosciences, University of Birmingham [Birmingham], Electrical Engineering and Computer Science Department - Case Western Reserve University, Case Western Reserve University [Cleveland], World Health Organization, Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Nordic School of Public Health, The James Hutton Institute, Horia Hulubei National Institute for Physics and Nuclear Engineering, Department of Pediatrics, University of Washington [Seattle], Institute of Public Health, Department of Physics, Clarendon Laboratory, Center for TeleInFrastruktur (CTIF), Aalborg University [Denmark] (AAU), Physikalisches Institut [Freiburg], Albert-Ludwigs-Universität Freiburg, Savoirs, Textes, Langage (STL) - UMR 8163 (STL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Dept.of Computer Science, Indian Institute of Technology Madras (IIT Madras), Istituto Mario Negri Bergamo, Centro Ricerche e Trapianti Villa Camozzi, Universidade Estadual Paulista Júlio de Mesquita Filho [São José do Rio Preto] (UNESP), Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut geològic de Catalunya (IGC), Institut Geològic de Catalunya-IGC, Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie et Génomique des Poissons (LPGP), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Recherche Agronomique (INRA), Politecnico di Milano [Milan] (POLIMI), Symantec, University of Oviedo, European Microsoft Innovation Center (EMIC), Microsoft Corporation [Redmond, Wash.], Technion - Israel Institute of Technology [Haifa], Laboratoire de Mécanique, Physique et Géosciences (LMPG), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU), Department of Physics, Cavendish Laboratory, University of Cambridge [UK] (CAM), Laboratoire de Probabilités et Modèles Aléatoires (LPMA), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Departments of Applied Physics [New Haven], Yale University [New Haven], Center for Mathematical Modeling (CMM), Department of Global Health and Development, London School of Hygiene and Tropical Medicine (LSHTM), University of Occupational and Environmental Health [Kitakyushu] (UEOH), Department of Computer Science and Engineering [New Delhi], Indian Institute of Technology Delhi (IIT Delhi), Institut de Recherche sur les Phénomènes Hors Equilibre (IRPHE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), GlaxoSmithKline, Imperial College London-Clinical Imaging Center, Universidade Federal de Pernambuco [Recife] (UFPE), Maclean Building, Benson Lane, Crowmarsh Gifford, Centre for Ecology and Hydrology, Nanoscience Institute (NEST), Dipartimento di Fisica, Università di Pisa, Aristotle University of Thessaloniki, Laboratory Of Immune Cell Biology (LICB), JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Occupational Health Unit, Bologna University Hospital-Sant'Orsola-Malpighi Polyclinic, Royal Institute of Technology [Stockholm] (KTH ), NICTA [Eveleigh], National ICT Australia [Sydney] (NICTA), Division of Solid Mechanics, Lund University [Lund], University of Calgary, BioWare Corp, Manchester Academic Health Sciences Centre, Institut d'Histoire et de Philosophie des Sciences et des Techniques (IHPST), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Ghent University [Belgium] (UGENT), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Children’s Hospital of Philadelphia (CHOP ), Perelman School of Medicine, University of Pennsylvania [Philadelphia], Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Department of Physiology, Augusta University - Medical College of Georgia, University System of Georgia (USG)-University System of Georgia (USG), Neurorestoration Group, Wolfson Centre for Age-related Diseases-King‘s College London, Electronic Navigation Research Institute (ENRI), Ministry of Land, Infrastructure, Transport and Tourism, Department of Computer Science [KAIST] (CS), Korea Advanced Institute of Science and Technology (KAIST), Centre for Undergraduate Studies, University of the Punjab, Siemens Corporate Research, Siemens AG [Munich], University of Massachusetts [Boston] (UMass Boston), University of Massachusetts System (UMASS), Department of Materials Science, Sichuan University [Chengdu] (SCU), Natl Engn Res Ctr Vegetables, Key Lab Biol & Genet Improvement Hort Crops N Chi, Beijing Acad Agr & Forestry Sci, University Hospital Puerta de Hierro, Madrid, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Université de Franche-Comté (UFC)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Technologie de Belfort-Montbeliard (UTBM), Shahid Beheshti University of Medical Sciences, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), MOLTECH-ANJOU (MOLTECH-ANJOU), Université d'Angers (UA)-Centre National de la Recherche Scientifique (CNRS), Universidad de Santiago de Chile [Santiago] (USACH)-Universidad del Desarrollo, Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), École supérieure du professorat et de l'éducation - Académie de Grenoble (ESPE Grenoble), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA), California Institute of Technology (CALTECH)-NASA, Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11), Université de la Méditerranée - Aix-Marseille 2-Institut National de la Recherche Agronomique (INRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Universidad de Santiago de Chile [Santiago] (USACH), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), King‘s College London-Wolfson Centre for Age-related Diseases, Sichuan University, Universitat de Barcelona, Interne Geneeskunde, Medische Sociologie, MUMC+: MA Psychiatrie (3), MUMC+: Hersen en Zenuw Centrum (3), Psychiatrie & Neuropsychologie, CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5)-Centre National de la Recherche Scientifique (CNRS), Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), RS: MHeNs - R2 - Mental Health, Laboratoire de Physique des Solides ( LPS ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de psychologie sociale et de psychologie cognitive ( LAPSCO ), Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ) -Centre National de la Recherche Scientifique ( CNRS ), Erosion torrentielle neige et avalanches ( UR ETGR ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), Hong Kong Baptist University ( HKBU ), Institut für Informatik [München/Munich] ( LMU ), Ludwig-Maximilians-Universität München, Neuroépidémiologie Tropicale ( NET ), CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Génomique, Environnement, Immunité, Santé, Thérapeutique ( GEIST ), Université de Limoges ( UNILIM ) -Université de Limoges ( UNILIM ), Insight Centre for Data Analytics (National University of Ireland Galway (NUIG)) ( INSIGHT ), Maladies chroniques, santé perçue, et processus d'adaptation ( APEMAC ), Université Paris Descartes - Paris 5 ( UPD5 ) -Université de Lorraine ( UL ), Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale ( INSERM ), University of Oslo ( UiO ) -University of Oslo ( UiO ), Centro de Estudios Avanzados en Zonas Aridas ( CEAZA ), Ecole Polytechnique Fédérale de Lausanne ( EPFL ), Laboratoire d'Ingénierie des Matériaux ( LIM ), Centre National de la Recherche Scientifique ( CNRS ), Lawrence Berkeley National Laboratory [Berkeley] ( LBNL ), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) ( SRI-B ), Computational Science and Engineering Department [Daresbury] ( STFC ), Multimedia Research Center ( MRC ), University of Alberta [Edmonton], Division of Biostatistics ( Biostat - MINNEAPOLIS ), University of Minnesota [Minneapolis], University of Southampton [Southampton], Interactions, transferts, ruptures artistiques et culturels - EA 6301 ( InTRu ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), University Medical Center Utrecht, Biologie Fonctionnelle et Adaptative ( BFA ), Imperial College London-Royal Brompton Hospital-National Heart and Lung Institute, Unité de recherche Virologie et Immunologie Moléculaires ( VIM ), Institut National de la Recherche Agronomique ( INRA ), National University of Singapore ( NUS ), Centre de Robotique ( CAOR ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), Department of Signal Theory and Communications ( TSC ), RESPEC ( RESPEC ), Advanced Laboratories on Embedded Systems [Roma] ( ALES ), Wageningen University and Research Centre [Wageningen] ( WUR ), Universitat Politècnica de Catalunya [Barcelona] ( UPC ), Institut National de Recherche et d'Analyse Physico-Chimique ( INRAP ), Institut National de Recherche et d'Analyse Physico-chimique (INRAP-Tunisie), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) ( FEMTO-ST ), Université de Franche-Comté ( UFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Supérieure de Mécanique et des Microtechniques ( ENSMM ) -Université de Technologie de Belfort-Montbeliard ( UTBM ), Tehran University, Sociétés, Acteurs, Gouvernement en Europe ( SAGE ), Université de Strasbourg ( UNISTRA ) -Centre National de la Recherche Scientifique ( CNRS ), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale ( SESSTIM - U912 INSERM - AMU - IRD ), Institut de Recherche pour le Développement ( IRD ) -Aix Marseille Université ( AMU ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Washington State University ( WSU ), Laboratoire de Physique de l'ENS Lyon ( Phys-ENS ), École normale supérieure - Lyon ( ENS Lyon ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), Institut des Sciences Chimiques de Rennes ( ISCR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Ecole Nationale Supérieure de Chimie de Rennes-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de recherche en informatique de Toulouse ( IRIT ), Institut National Polytechnique [Toulouse] ( INP ) -Université Toulouse 1 Capitole ( UT1 ) -Université Toulouse - Jean Jaurès ( UT2J ) -Université Toulouse III - Paul Sabatier ( UPS ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique ( CNRS ), Centre de Recherche en Automatique de Nancy ( CRAN ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Université Catholique de Louvain ( UCL ), Freie Universität Berlin [Berlin], Div Cyclotron & Radiopharmaceut Sci ( DRDO, INMAS ), Institut de Physique Nucléaire d'Orsay ( IPNO ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Tata Research Development and Design Center ( TRDDC ), Laboratoire MOLTECH-Anjou [Angers] ( MOLTECH ANJOU ), Université d'Angers ( UA ) -Centre National de la Recherche Scientifique ( CNRS ), University of Helsinki [Helsinki], Swedish Defense Research Agency ( FOI ), Servicio de Neurologia ( SANTIAGO - Neurologie ), Universidad de Santiago de Chile [Santiago] ( USACH ) -Universidad del Desarrollo, Novartis Pharmaceutical Corp., East Hanover NJ 07936, USA, Laboratoire de glaciologie et géophysique de l'environnement ( LGGE ), Observatoire des Sciences de l'Univers de Grenoble ( OSUG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Mechanical and Automation Engineering ( CAD Laboratory ), National Institutes of Health ( NIH ), Centre d'étude et de recherche en informatique et communications ( CEDRIC ), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise ( ENSIIE ) -Conservatoire National des Arts et Métiers [CNAM] ( CNAM ), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] ( Heudiasyc ), Université de Technologie de Compiègne ( UTC ) -Centre National de la Recherche Scientifique ( CNRS ), College of Information and Electrical Engineering [Beijing] ( CIEE ), China Agricultural University ( CAU ), Queen Mary University of London ( QMUL ), Centre d'économie de la Sorbonne ( CES ), Université Panthéon-Sorbonne ( UP1 ) -Centre National de la Recherche Scientifique ( CNRS ), Paris School of Economics ( PSE ), École supérieure du professorat et de l'éducation - Académie de Grenoble ( ESPE Grenoble ), Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -Université Grenoble Alpes ( UGA ), PennState University [Pennsylvania] ( PSU ), Jet Propulsion Laboratory ( JPL ), NASA-California Institute of Technology ( CALTECH ), American University of Beirut [Beyrouth], The University of Sydney [Sydney], Département Optique ( OPT ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des signaux et systèmes ( L2S ), Université Paris-Sud - Paris 11 ( UP11 ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Center for Research in Environmental Epidemiology ( CREAL ), Université de la Méditerranée - Aix-Marseille 2-Institut National de la Recherche Agronomique ( INRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Center for TeleInFrastruktur ( CTIF ), Aalborg University [Denmark] ( AAU ), Savoirs, Textes, Langage (STL) - UMR 8163 ( STL ), Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Indian Institute of Technology Madras ( IIT Madras ), Universidade Estadual Paulista Júlio de Mesquita ( UNESP ), Laboratoire de Génie Informatique et Ingénierie de Production ( LGI2P ), IMT - Mines Alès Ecole Mines - Télécom ( IMT - MINES ALES ), Institut geològic de Catalunya ( IGC ), Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Physiologie et Génomique des Poissons ( LPGP ), Institut National de la Recherche Agronomique ( INRA ) -Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Politecnico di Milano [Milan], European Microsoft Innovation Center ( EMIC ), Laboratoire de Mécanique, Physique et Géosciences ( LMPG ), Université Le Havre Normandie ( ULH ), Normandie Université ( NU ) -Normandie Université ( NU ), University of Cambridge [UK] ( CAM ), Laboratoire de Probabilités et Modèles Aléatoires ( LPMA ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Génétique et de Biologie Moléculaire et Cellulaire ( IGBMC ), Université de Strasbourg ( UNISTRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Center for Mathematical Modeling ( CMM ), Universidad de Santiago de Chile [Santiago] ( USACH ), London School of Hygiene and Tropical Medicine ( LSHTM ), University of Occupational and Environmental Health [Kitakyushu] ( UEOH ), Indian Institute of Technology Delhi ( IIT Delhi ), Institut de Recherche sur les Phénomènes Hors Equilibre ( IRPHE ), Aix Marseille Université ( AMU ) -Ecole Centrale de Marseille ( ECM ) -Centre National de la Recherche Scientifique ( CNRS ), Universidade Federal de Pernambuco [Recife] ( UFPE ), Nanoscience Institute ( NEST ), Laboratory Of Immune Cell Biology ( LICB ), JRC Institute for Environment and Sustainability ( IES ), European Commission - Joint Research Centre [Ispra] ( JRC ), Bonn Universität [Bonn], Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] ( LNCC / MCT ), Royal Institute of Technology [Stockholm] ( KTH ), National ICT Australia [Sydney] ( NICTA ), Institut d'Histoire et de Philosophie des Sciences et des Techniques ( IHPST ), Université Panthéon-Sorbonne ( UP1 ) -Département d'Etudes Cognitives - ENS Paris ( DEC ), École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ), Ghent University [Belgium] ( UGENT ), Children’s Hospital of Philadelphia ( CHOP ), Univeristy of Pennsylvania Medical School, Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique ( LHEEA ), École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche en Génie Civil et Mécanique ( GeM ), Université de Nantes ( UN ) -École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Medical College of Georgia, Electronic Navigation Research Institute ( ENRI ), Department of Computer Science [KAIST] ( CS ), Korea Advanced Institute of Science and Technology ( KAIST ), Laboratoire de l'Accélérateur Linéaire ( LAL ), University of Massachusetts [Boston] ( UMass Boston ), Forouzanfar, Mohammad H, Alexander, Lily, Anderson, H. Ro, Bachman, Victoria F, Biryukov, Stan, Brauer, Michael, Burnett, Richard, Casey, Daniel, Coates, Matthew M, Cohen, Aaron, Delwiche, Kristen, Estep, Kara, Frostad, Joseph J, Kc, Astha, Kyu, Hmwe H, Moradi Lakeh, Maziar, Ng, Marie, Slepak, Erica Leigh, Thomas, Bernadette A, Wagner, Joseph, Aasvang, Gunn Marit, Abbafati, Cristiana, Ozgoren, Ayse Abbasoglu, Abd Allah, Foad, Abera, Semaw F, Aboyans, Victor, Abraham, Biju, Abraham, Jerry Puthenpurakal, Abubakar, Ibrahim, Abu Rmeileh, Niveen M. E, Aburto, Tania C, Achoki, Tom, Adelekan, Ademola, Adofo, Koranteng, Adou, Arsène K, Adsuar, José C, Afshin, Ashkan, Agardh, Emilie E, Al Khabouri, Mazin J, Al Lami, Faris H, Alam, Sayed Saidul, Alasfoor, Deena, Albittar, Mohammed I, Alegretti, Miguel A, Aleman, Alicia V, Alemu, Zewdie A, Alfonso Cristancho, Rafael, Alhabib, Samia, Ali, Raghib, Ali, Mohammed K, Alla, Françoi, Allebeck, Peter, Allen, Peter J, Alsharif, Ubai, Alvarez, Elena, Alvis Guzman, Nelson, Amankwaa, Adansi A, Amare, Azmeraw T, Ameh, Emmanuel A, Ameli, Omid, Amini, Heresh, Ammar, Walid, Anderson, Benjamin O, Antonio, Carl Abelardo T, Anwari, Palwasha, Cunningham, Solveig Argeseanu, Arnlöv, Johan, Arsenijevic, Valentina S. Arsic, Artaman, Al, Asghar, Rana J, Assadi, Reza, Atkins, Lydia S, Atkinson, Charle, Avila, Marco A, Awuah, Baffour, Badawi, Alaa, Bahit, Maria C, Bakfalouni, Talal, Balakrishnan, Kalpana, Balalla, Shivanthi, Balu, Ravi Kumar, Banerjee, Amitava, Barber, Ryan M, Barker Collo, Suzanne L, Barquera, Simon, Barregard, Lar, Barrero, Lope H, Barrientos Gutierrez, Tonatiuh, Basto Abreu, Ana C, Basu, Arindam, Basu, Sanjay, Basulaiman, Mohammed O, Ruvalcaba, Carolina Bati, Beardsley, Justin, Bedi, Neeraj, Bekele, Tolesa, Bell, Michelle L, Benjet, Corina, Bennett, Derrick A, Benzian, Habib, Bernabé, Eduardo, Beyene, Tariku J, Bhala, Neeraj, Bhalla, Ashish, Bhutta, Zulfiqar A, Bikbov, Bori, Abdulhak, Aref A. Bin, Blore, Jed D, Blyth, Fiona M, Bohensky, Megan A, Başara, Berrak Bora, Borges, Guilherme, Bornstein, Natan M, Bose, Dipan, Boufous, Soufiane, Bourne, Rupert R, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J, Brenner, Hermann, Briggs, Adam D. M, Broday, David M, Brooks, Peter M, Bruce, Nigel G, Brugha, Traolach S, Brunekreef, Bert, Buchbinder, Rachelle, Bui, Linh N, Bukhman, Gene, Bulloch, Andrew G, Burch, Michael, Burney, Peter G. J, Campos Nonato, Ismael R, Campuzano, Julio C, Cantoral, Alejandra J, Caravanos, Jack, Cárdenas, Rosario, Cardis, Elisabeth, Carpenter, David O, Caso, Valeria, Castañeda Orjuela, Carlos A, Castro, Ruben E, Catalá López, Ferrán, Cavalleri, Fiorella, Çavlin, Alanur, Chadha, Vineet K, Chang, Jung Chen, Charlson, Fiona J, Chen, Honglei, Chen, Wanqing, Chen, Zhengming, Chiang, Peggy P, Chimed Ochir, Odgerel, Chowdhury, Rajiv, Christophi, Costas A, Chuang, Ting Wu, Chugh, Sumeet S, Cirillo, Massimo, Claßen, Thomas Kd, Colistro, Valentina, Colomar, Mercede, Colquhoun, Samantha M, Contreras, Alejandra G, Cooper, Cyru, Cooperrider, Kimberly, Cooper, Leslie T, Coresh, Josef, Courville, Karen J, Criqui, Michael H, Cuevas Nasu, Lucia, Damsere Derry, Jame, Danawi, Hadi, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I, Davis, Adrian, Davitoiu, Dragos V, Dayama, Anand, de Castro, E. Filipa, De la Cruz Góngora, Vanessa, De Leo, Diego, de Lima, Graça, Degenhardt, Louisa, Del Pozo Cruz, Borja, Dellavalle, Robert P, Deribe, Kebede, Derrett, Sarah, Jarlais, Don C. De, Dessalegn, Muluken, Deveber, Gabrielle A, Devries, Karen M, Dharmaratne, Samath D, Dherani, Mukesh K, Dicker, Daniel, Ding, Eric L, Dokova, Klara, Dorsey, E. Ray, Driscoll, Tim R, Duan, Leilei, Durrani, Adnan M, Ebel, Beth E, Ellenbogen, Richard G, Elshrek, Yousef M, Endres, Matthia, Ermakov, Sergey P, Erskine, Holly E, Eshrati, Babak, Esteghamati, Alireza, Fahimi, Saman, Faraon, Emerito Jose A, Farzadfar, Farshad, Fay, Derek F. J, Feigin, Valery L, Feigl, Andrea B, Fereshtehnejad, Seyed Mohammad, Ferrari, Alize J, Ferri, Cleusa P, Flaxman, Abraham D, Fleming, Thomas D, Foigt, Nataliya, Foreman, Kyle J, Paleo, Urbano Fra, Franklin, Richard C, Gabbe, Belinda, Gaffikin, Lynne, Gakidou, Emmanuela, Gamkrelidze, Amiran, Gankpé, Fortuné G, Gansevoort, Ron T, García Guerra, Francisco A, Gasana, Evariste, Geleijnse, Johanna M, Gessner, Bradford D, Gething, Pete, Gibney, Katherine B, Gillum, Richard F, Ginawi, Ibrahim A. M, Giroud, Maurice, Giussani, Giorgia, Goenka, Shifalika, Goginashvili, Ketevan, Dantes, Hector Gomez, Gona, Philimon, de Cosio, Teresita Gonzalez, González Castell, Dinorah, Gotay, Carolyn C, Goto, Atsushi, Gouda, Hebe N, Guerrant, Richard L, Gugnani, Harish C, Guillemin, Franci, Gunnell, David, Gupta, Rahul, Gupta, Rajeev, Gutiérrez, Reyna A, Hafezi Nejad, Nima, Hagan, Holly, Hagstromer, Maria, Halasa, Yara A, Hamadeh, Randah R, Hammami, Mouhanad, Hankey, Graeme J, Hao, Yuantao, Harb, Hilda L, Haregu, Tilahun Nigatu, Haro, Josep Maria, Havmoeller, Rasmu, Hay, Simon I, Hedayati, Mohammad T, Heredia Pi, Ileana B, Hernandez, Lucia, Heuton, Kyle R, Heydarpour, Pouria, Hijar, Martha, Hoek, Hans W, Hoffman, Howard J, Hornberger, John C, Hosgood, H. Dean, Hoy, Damian G, Hsairi, Mohamed, Hu, Guoqing, Hu, Howard, Huang, Cheng, Huang, John J, Hubbell, Bryan J, Huiart, Laetitia, Husseini, Abdullatif, Iannarone, Marissa L, Iburg, Kim M, Idrisov, Bulat T, Ikeda, Nayu, Innos, Kaire, Inoue, Manami, Islami, Farhad, Ismayilova, Samaya, Jacobsen, Kathryn H, Jansen, Henrica A, Jarvis, Deborah L, Jassal, Simerjot K, Jauregui, Alejandra, Jayaraman, Sudha, Jeemon, Panniyammakal, Jensen, Paul N, Jha, Vivekanand, Jiang, Fan, Jiang, Guohong, Jiang, Ying, Jonas, Jost B, Juel, Knud, Kan, Haidong, Roseline, Sidibe S. Kany, Karam, Nadim E, Karch, André, Karema, Corine K, Karthikeyan, Ganesan, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S, Kemp, Andrew H, Kengne, Andre P, Keren, Andre, Khader, Yousef S, Khalifa, Shams Eldin Ali Hassan, Khan, Ejaz A, Khang, Young Ho, Khatibzadeh, Shahab, Khonelidze, Irma, Kieling, Christian, Kim, Daniel, Kim, Sungroul, Kim, Yunjin, Kimokoti, Ruth W, Kinfu, Yohanne, Kinge, Jonas M, Kissela, Brett M, Kivipelto, Miia, Knibbs, Luke D, Knudsen, Ann Kristin, Kokubo, Yoshihiro, Kose, M. Rifat, Kosen, Soewarta, Kraemer, Alexander, Kravchenko, Michael, Krishnaswami, Sanjay, Kromhout, Han, Ku, Tiffany, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kuipers, Ernst J, Kulkarni, Chanda, Kulkarni, Veena S, Kumar, G. Anil, Kwan, Gene F, Lai, Taavi, Balaji, Arjun Lakshmana, Lalloo, Ratilal, Lallukka, Tea, Lam, Hilton, Lan, Qing, Lansingh, Van C, Larson, Heidi J, Larsson, Ander, Laryea, Dennis O, Lavados, Pablo M, Lawrynowicz, Alicia E, Leasher, Janet L, Lee, Jong Tae, Leigh, Jame, Leung, Ricky, Levi, Miriam, Li, Yichong, Li, Yongmei, Liang, Juan, Liang, Xiaofeng, Lim, Stephen S, Lindsay, M. Patrice, Lipshultz, Steven E, Liu, Shiwei, Liu, Yang, Lloyd, Belinda K, Logroscino, Giancarlo, London, Stephanie J, Lopez, Nancy, Lortet Tieulent, Joannie, Lotufo, Paulo A, Lozano, Rafael, Lunevicius, Raimunda, Ma, Jixiang, Ma, Stefan, Machado, Vasco M. P, Macintyre, Michael F, Magis Rodriguez, Carlo, Mahdi, Abbas A, Majdan, Marek, Malekzadeh, Reza, Mangalam, Srikanth, Mapoma, Christopher C, Marape, Marape, Marcenes, Wagner, Margolis, David J, Margono, Christopher, Marks, Guy B, Martin, Randall V, Marzan, Melvin B, Mashal, Mohammad T, Masiye, Felix, Mason Jones, Amanda J, Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M, Mazorodze, Tasara T, Mckay, Abigail C, Mckee, Martin, Mclain, Abigail, Meaney, Peter A, Medina, Catalina, Mehndiratta, Man Mohan, Mejia Rodriguez, Fabiola, Mekonnen, Wubegzier, Melaku, Yohannes A, Meltzer, Michele, Memish, Ziad A, Mendoza, Walter, Mensah, George A, Meretoja, Atte, Mhimbira, Francis Apolinary, Micha, Renata, Miller, Ted R, Mills, Edward J, Misganaw, Awoke, Mishra, Santosh, Ibrahim, Norlinah Mohamed, Mohammad, Karzan A, Mokdad, Ali H, Mola, Glen L, Monasta, Lorenzo, Hernandez, Julio C. Montañez, Montico, Marcella, Moore, Ami R, Morawska, Lidia, Mori, Rintaro, Moschandreas, Joanna, Moturi, Wilkister N, Mozaffarian, Dariush, Mueller, Ulrich O, Mukaigawara, Mitsuru, Mullany, Erin C, Murthy, Kinnari S, Naghavi, Mohsen, Nahas, Ziad, Naheed, Aliya, Naidoo, Kovin S, Naldi, Luigi, Nand, Devina, Nangia, Vinay, Narayan, Km Venkat, Nash, Deni, Neal, Bruce, Nejjari, Chakib, Neupane, Sudan P, Newton, Charles R, Ngalesoni, Frida N, de Dieu Ngirabega, Jean, Nguyen, Grant, Nguyen, Nhung T, Nieuwenhuijsen, Mark J, Nisar, Muhammad I, Nogueira, José R, Nolla, Joan M, Nolte, Sandra, Norheim, Ole F, Norman, Rosana E, Norrving, Bo, Nyakarahuka, Luke, Oh, In Hwan, Ohkubo, Takayoshi, Olusanya, Bolajoko O, Omer, Saad B, Opio, John Nelson, Orozco, Ricardo, Pagcatipunan, Rodolfo S, Pain, Amanda W, Pandian, Jeyaraj D, Panelo, Carlo Irwin A, Papachristou, Christina, Park, Eun Kee, Parry, Charles D, Caicedo, Angel J. Paternina, Patten, Scott B, Paul, Vinod K, Pavlin, Boris I, Pearce, Neil, Pedraza, Lilia S, Pedroza, Andrea, Stokic, Ljiljana Pejin, Pekericli, Ayfer, Pereira, David M, Perez Padilla, Rogelio, Perez Ruiz, Fernando, Perico, Norberto, Perry, Samuel A. L, Pervaiz, Aslam, Pesudovs, Konrad, Peterson, Carrie B, Petzold, Max, Phillips, Michael R, Phua, Hwee Pin, Plass, Dietrich, Poenaru, Dan, Polanczyk, Guilherme V, Polinder, Suzanne, Pond, Constance D, Pope, C. Arden, Pope, Daniel, Popova, Svetlana, Pourmalek, Farshad, Powles, John, Prabhakaran, Dorairaj, Prasad, Noela M, Qato, Dima M, Quezada, Amado D, Quistberg, D. Alex A, Racapé, Lionel, Rafay, Anwar, Rahimi, Kazem, Rahimi Movaghar, Vafa, Rahman, Sajjad Ur, Raju, Murugesan, Rakovac, Ivo, Rana, Saleem M, Rao, Mayuree, Razavi, Homie, Reddy, K. Srinath, Refaat, Amany H, Rehm, Jürgen, Remuzzi, Giuseppe, Ribeiro, Antonio L, Riccio, Patricia M, Richardson, Lee, Riederer, Anne, Robinson, Margaret, Roca, Anna, Rodriguez, Alina, Rojas Rueda, David, Romieu, Isabelle, Ronfani, Luca, Room, Robin, Roy, Nobhojit, Ruhago, George M, Rushton, Lesley, Sabin, Nsanzimana, Sacco, Ralph L, Saha, Sukanta, Sahathevan, Ramesh, Sahraian, Mohammad Ali, Salomon, Joshua A, Salvo, Deborah, Sampson, Uchechukwu K, Sanabria, Juan R, Sanchez, Luz Maria, Sánchez Pimienta, Tania G, Sanchez Riera, Lidia, Sandar, Logan, Santos, Itamar S, Sapkota, Amir, Satpathy, Maheswar, Saunders, James E, Sawhney, Monika, Saylan, Mete I, Scarborough, Peter, Schmidt, Jürgen C, Schneider, Ione J. C, Schöttker, Ben, Schwebel, David C, Scott, James G, Seedat, Soraya, Sepanlou, Sadaf G, Serdar, Berrin, Servan Mori, Edson E, Shaddick, Gavin, Shahraz, Saeid, Levy, Teresa Shamah, Shangguan, Siyi, She, Jun, Sheikhbahaei, Sara, Shibuya, Kenji, Shin, Hwashin H, Shinohara, Yukito, Shiri, Rahman, Shishani, Kawkab, Shiue, Ivy, Sigfusdottir, Inga D, Silberberg, Donald H, Simard, Edgar P, Sindi, Shireen, Singh, Abhishek, Singh, Gitanjali M, Singh, Jasvinder A, Skirbekk, Vegard, Sliwa, Karen, Soljak, Michael, Soneji, Samir, Søreide, Kjetil, Soshnikov, Sergey, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Stapelberg, Nicolas J. C, Stathopoulou, Vasiliki, Steckling, Nadine, Stein, Dan J, Stein, Murray B, Stephens, Natalie, Stöckl, Heidi, Straif, Kurt, Stroumpoulis, Konstantino, Sturua, Lela, Sunguya, Bruno F, Swaminathan, Soumya, Swaroop, Mamta, Sykes, Bryan L, Tabb, Karen M, Takahashi, Ken, Talongwa, Roberto T, Tandon, Nikhil, Tanne, David, Tanner, Marcel, Tavakkoli, Mohammad, Te Ao, Braden J, Teixeira, Carolina M, Téllez Rojo, Martha M, Terkawi, Abdullah S, Texcalac Sangrador, José Lui, Thackway, Sarah V, Thomson, Blake, Thorne Lyman, Andrew L, Thrift, Amanda G, Thurston, George D, Tillmann, Taavi, Tobollik, Myriam, Tonelli, Marcello, Topouzis, Foti, Towbin, Jeffrey A, Toyoshima, Hideaki, Traebert, Jefferson, Tran, Bach X, Trasande, Leonardo, Trillini, Matia, Trujillo, Ulise, Dimbuene, Zacharie Tsala, Tsilimbaris, Miltiadi, Tuzcu, Emin Murat, Uchendu, Uche S, Ukwaja, Kingsley N, Uzun, Selen B, van de Vijver, Steven, Van Dingenen, Rita, van Gool, Coen H, van Os, Jim, Varakin, Yuri Y, Vasankari, Tommi J, Vasconcelos, Ana Maria N, Vavilala, Monica S, Veerman, Lennert J, Velasquez Melendez, Gustavo, Venketasubramanian, N, Vijayakumar, Lakshmi, Villalpando, Salvador, Violante, Francesco S, Vlassov, Vasiliy Victorovich, Vollset, Stein Emil, Wagner, Gregory R, Waller, Stephen G, Wallin, Mitchell T, Wan, Xia, Wang, Haidong, Wang, Jianli, Wang, Linhong, Wang, Wenzhi, Wang, Yanping, Warouw, Tati S, Watts, Charlotte H, Weichenthal, Scott, Weiderpass, Elisabete, Weintraub, Robert G, Werdecker, Andrea, Wessells, K. Ryan, Westerman, Ronny, Whiteford, Harvey A, Wilkinson, James D, Williams, Hywel C, Williams, Thomas N, Woldeyohannes, Solomon M, Wolfe, Charles D. A, Wong, John Q, Woolf, Anthony D, Wright, Jonathan L, Wurtz, Brittany, Xu, Gelin, Yan, Lijing L, Yang, Gonghuan, Yano, Yuichiro, Ye, Pengpeng, Yenesew, Muluken, Yentür, Gökalp K, Yip, Paul, Yonemoto, Naohiro, Yoon, Seok Jun, Younis, Mustafa Z, Younoussi, Zourkaleini, Yu, Chuanhua, Zaki, Maysaa E, Zhao, Yong, Zheng, Yingfeng, Zhou, Maigeng, Zhu, Jun, Zhu, Shankuan, Zou, Xiaonong, Zunt, Joseph R, Lopez, Alan D, Vos, Theo, Murray, Christopher J., Cell biology, Epidemiology, Neurosciences, Health Technology Assessment (HTA), Public Health, General practice, Bachman, Victoria F., Coates, Matthew M., Frostad, Joseph J., Astha, K.C., Kyu, Hmwe H., Moradi-Lakeh, Maziar, Thomas, Bernadette A., Abbasoglu Ozgoren, Ayse, Abd-Allah, Foad, Abera, Semaw F., Puthenpurakal Abraham, Jerry, Abu-Rmeileh, Niveen M.E., Aburto, Tania C., Adou, Arsène K., Adsuar, José C., Agardh, Emilie E., Al Khabouri, Mazin J., Al Lami, Faris H., Albittar, Mohammed I., Alegretti, Miguel A., Aleman, Alicia V., Alemu, Zewdie A., Alfonso-Cristancho, Rafael, Ali, Mohammed K., Allen, Peter J., Alvis-Guzman, Nelson, Amankwaa, Adansi A., Amare, Azmeraw T., Ameh, Emmanuel A., Anderson, Benjamin O., Antonio, Carl Abelardo T., Argeseanu Cunningham, Solveig, Arsic Arsenijevic, Valentina S., Asghar, Rana J., Atkins, Lydia S., Avila, Marco A., Bahit, Maria C., Barber, Ryan M., Barker-Collo, Suzanne L., Barrero, Lope H., Barrientos-Gutierrez, Tonatiuh, Basto-Abreu, Ana C., Basulaiman, Mohammed O., Batis Ruvalcaba, Carolina, Bell, Michelle L., Bennett, Derrick A., Beyene, Tariku J., Bhutta, Zulfiqar A., Bin Abdulhak, Aref A., Blore, Jed D., Blyth, Fiona M., Bohensky, Megan A., Bora Başara, Berrak, Bornstein, Natan M., Bourne, Rupert R., Breitborde, Nicholas J., Briggs, Adam D.M., Broday, David M., Brooks, Peter M., Bruce, Nigel G., Brugha, Traolach S., Bui, Linh N., Bulloch, Andrew G., Burney, Peter G.J., Campos-Nonato, Ismael R., Campuzano, Julio C., Cantoral, Alejandra J., Carpenter, David O., Castañeda-Orjuela, Carlos A., Castro, Ruben E., Catalá-López, Ferrán, Chadha, Vineet K., Chang, Jung-Chen, Charlson, Fiona J., Chiang, Peggy P., Chimed-Ochir, Odgerel, Christophi, Costas A., Chuang, Ting-Wu, Chugh, Sumeet S., Claßen, Thomas K.D., Colquhoun, Samantha M., Contreras, Alejandra G., Cooper, Leslie T., Courville, Karen J., Criqui, Michael H., Cuevas-Nasu, Lucia, Damsere-Derry, Jame, Dargan, Paul I., Davitoiu, Dragos V., De Castro, E. Filipa, De La Cruz-Góngora, Vanessa, De Lima, Graça, Del Pozo-Cruz, Borja, Dellavalle, Robert P., Des Jarlais, Don C., Deveber, Gabrielle A., Devries, Karen M., Dharmaratne, Samath D., Dherani, Mukesh K., Ding, Eric L., Driscoll, Tim R., Durrani, Adnan M., Ebel, Beth E., Ellenbogen, Richard G., Elshrek, Yousef M., Ermakov, Sergey P., Erskine, Holly E., Faraon, Emerito Jose A., Fay, Derek F.J., Feigin, Valery L., Feigl, Andrea B., Fereshtehnejad, Seyed-Mohammad, Ferrari, Alize J., Ferri, Cleusa P., Flaxman, Abraham D., Fleming, Thomas D., Foreman, Kyle J., Fra Paleo, Urbano, Franklin, Richard C., Gankpé, Fortuné G., Gansevoort, Ron T., García-Guerra, Francisco A., Geleijnse, Johanna M., Gessner, Bradford D., Gibney, Katherine B., Gillum, Richard F., Ginawi, Ibrahim A.M., Gomez Dantes, Hector, Gonzalez De Cosio, Teresita, González-Castell, Dinorah, Gotay, Carolyn C., Gouda, Hebe N., Guerrant, Richard L., Gugnani, Harish C., Gutiérrez, Reyna A., Hafezi-Nejad, Nima, Halasa, Yara A., Hamadeh, Randah R., Hankey, Graeme J., Harb, Hilda L., Hay, Simon I., Hedayati, Mohammad T., Heredia-Pi, Ileana B., Heuton, Kyle R., Hoek, Hans W., Hoffman, Howard J., Hornberger, John C., Hosgood, H., Hoy, Damian G., Huang, John J., Hubbell, Bryan J., Iannarone, Marissa L., Iburg, Kim M., Idrisov, Bulat T., Jacobsen, Kathryn H., Jansen, Henrica A., Jarvis, Deborah L., Jassal, Simerjot K., Jensen, Paul N., Jonas, Jost B., Kany Roseline, Sidibe S., Karam, Nadim E., Karema, Corine K., Kazi, Dhruv S., Kemp, Andrew H., Kengne, Andre P., Khader, Yousef S., Ali Hassan Khalifa, Shams Eldin, Khan, Ejaz A., Khang, Young-Ho, Kimokoti, Ruth W., Kinge, Jonas M., Kissela, Brett M., Knibbs, Luke D., Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kuipers, Ernst J., Kulkarni, Veena S., Kwan, Gene F., Lakshmana Balaji, Arjun, Lansingh, Van C., Larson, Heidi J., Laryea, Dennis O., Lavados, Pablo M., Lawrynowicz, Alicia E., Leasher, Janet L., Lee, Jong-Tae, Lim, Stephen S., Lipshultz, Steven E., Lloyd, Belinda K., London, Stephanie J., Lortet-Tieulent, Joannie, Lotufo, Paulo A., Machado, Vasco M.P., Macintyre, Michael F., Magis-Rodriguez, Carlo, Mahdi, Abbas A., Mapoma, Christopher C., Margolis, David J., Marks, Guy B., Martin, Randall V., Marzan, Melvin B., Mashal, Mohammad T., Mason-Jones, Amanda J., Mayosi, Bongani M., Mazorodze, Tasara T., Mckay, Abigail C., Meaney, Peter A., Mejia-Rodriguez, Fabiola, Melaku, Yohannes A., Memish, Ziad A., Mensah, George A., Apolinary Mhimbira, Franci, Miller, Ted R., Mills, Edward J., Mohamed Ibrahim, Norlinah, Mohammad, Karzan A., Mokdad, Ali H., Mola, Glen L., Montañez Hernandez, Julio C., Moore, Ami R., Moturi, Wilkister N., Mueller, Ulrich O., Mullany, Erin C., Murthy, Kinnari S., Naidoo, Kovin S., Narayan, K.M. Venkat, Neupane, Sudan P., Newton, Charles R., Ngalesoni, Frida N., Ngirabega, Jean De Dieu, Nguyen, Nhung T., Nieuwenhuijsen, Mark J., Nisar, Muhammad I., Nogueira, José R., Nolla, Joan M., Norheim, Ole F., Norman, Rosana E., Oh, In-Hwan, Olusanya, Bolajoko O., Omer, Saad B., Pagcatipunan, Rodolfo S., Pain, Amanda W., Pandian, Jeyaraj D., Panelo, Carlo Irwin A., Park, Eun-Kee, Parry, Charles D., Paternina Caicedo, Angel J., Patten, Scott B., Paul, Vinod K., Pavlin, Boris I., Pedraza, Lilia S., Pejin Stokic, Ljiljana, Pereira, David M., Perez-Padilla, Rogelio, Perez-Ruiz, Fernando, Perry, Samuel A.L., Phillips, Michael R., Polanczyk, Guilherme V., Pond, Constance D., Prasad, Noela M., Qato, Dima M., Quezada, Amado D., Quistberg, D. Alex A., Rahimi-Movaghar, Vafa, Ur Rahman, Sajjad, Rana, Saleem M., Refaat, Amany H., Ribeiro, Antonio L., Riccio, Patricia M., Rojas-Rueda, David, Ruhago, George M., Sacco, Ralph L., Salomon, Joshua A., Sampson, Uchechukwu K., Sanabria, Juan R., Sánchez-Pimienta, Tania G., Sanchez-Riera, Lidia, Santos, Itamar S., Saunders, James E., Saylan, Mete I., Schmidt, Jürgen C., Schneider, Ione J.C., Schwebel, David C., Scott, James G., Sepanlou, Sadaf G., Servan-Mori, Edson E., Shamah Levy, Teresa, Shin, Hwashin H., Sigfusdottir, Inga D., Silberberg, Donald H., Simard, Edgar P., Singh, Gitanjali M., Singh, Jasvinder A., Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Stapelberg, Nicolas J.C., Stein, Dan J., Stein, Murray B., Sunguya, Bruno F., Sykes, Bryan L., Tabb, Karen M., Talongwa, Roberto T., Te Ao, Braden J., Teixeira, Carolina M., Téllez Rojo, Martha M., Terkawi, Abdullah S., Texcalac-Sangrador, José Lui, Thackway, Sarah V., Thorne-Lyman, Andrew L., Thrift, Amanda G., Thurston, George D., Towbin, Jeffrey A., Tran, Bach X., Tsala Dimbuene, Zacharie, Uchendu, Uche S., Ukwaja, Kingsley N., Uzun, Selen B., Van De Vijver, Steven, Van Gool, Coen H., Van Os, Jim, Varakin, Yuri Y., Vasankari, Tommi J., Vasconcelos, Ana Maria N., Vavilala, Monica S., Veerman, Lennert J., Velasquez-Melendez, Gustavo, Venketasubramanian, N., Violante, Francesco S., Victorovich Vlassov, Vasiliy, Wagner, Gregory R., Waller, Stephen G., Wallin, Mitchell T., Warouw, Tati S., Watts, Charlotte H., Weintraub, Robert G., Whiteford, Harvey A., Wilkinson, James D., Williams, Hywel C., Williams, Thomas N., Woldeyohannes, Solomon M., Wolfe, Charles D.A., Wong, John Q., Woolf, Anthony D., Wright, Jonathan L., Yan, Lijing L., Yentür, Gökalp K., Yoon, Seok-Jun, Younis, Mustafa Z., Zaki, Maysaa E., Zunt, Joseph R., Lopez, Alan D., and Temesgen, A.M.

Subjects
Male, Fine particulate matter, Nutrition and Disease, MESH : Sanitation, Health Behavior, Diseases, MESH: Metabolic Diseases, MESH: Global Health, 030204 cardiovascular system & hematology, MESH: Risk Assessment, Global Health, MESH : Nutritional Status, MESH: Occupational Exposure, 0302 clinical medicine, Unsafe Sex, MESH: Risk Factors, Risk Factors, Voeding en Ziekte, Medicine, Air-pollution, MESH : Female, 030212 general & internal medicine, MESH : Risk Assessment, Sanitation, Wasting, 2. Zero hunger, Factors de risc en les malalties, Medicine (all), [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, Public Health, Global Health, Social Medicine and Epidemiology, General Medicine, MESH : Occupational Diseases, MESH: Nutritional Status, All-cause mortality, MESH : Risk Factors, humanities, Environmental Exposure, Female, Humans, Metabolic Diseases, Nutritional Status, Occupational Diseases, Occupational Exposure, Risk Assessment, Tobacco smoking, 3. Good health, Nutritional Statu, MESH : Occupational Exposure, MESH : Metabolic Diseases, Cohort, medicine.symptom, Risk assessment, Blood-pressure, Human, MESH: Occupational Diseases, Risk factors in diseases, Coronary-heart-disease, MESH : Male, MESH: Health Behavior, MESH: Environmental Exposure, Population health, Body-mass index, 03 medical and health sciences, Household cooking, Cardiovascular-disease, Environmental health, General & Internal Medicine, parasitic diseases, Life Science, MESH: Sanitation, Risk factor, MESH : Health Behavior, VLAG, GBD2013, MESH: Humans, business.industry, Risk Factor, Global Burden of Disease Study, 79 behavioural, environmental and occupational, and metabolic risks, Long-term exposure, MESH : Humans, CAUSE-SPECIFIC MORTALITY, MESH: Male, Metabolic Disease, Occupational Disease, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, MALE BRITISH DOCTORS, Years of potential life lost, Relative risk, Malalties, MESH : Global Health, OUTDOOR AIR-POLLUTION, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, MESH : Environmental Exposure, MESH: Female
Abstract

Summary Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8–58·5) of deaths and 41·6% (40·1–43·0) of DALYs. Risks quantified account for 87·9% (86·5−89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks. Funding Bill & Melinda Gates Foundation. Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the fi rst of a series of annual updates of the GBD. Risk factor quantifi cation, particularly of modifi able risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the fi rst level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular fi ltration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8–58·5) of deaths and 41·6% (40·1–43·0) of DALYs. Risks quantified account for 87·9% (86·5−89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks. Funding Bill & Melinda Gates Foundation.

Published
2015
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24. Magnetic Resonance Imaging of the Heart and Large Vessels -- Survey of methods and new perspectives

Author

Chabiniok, Radomir, Súkupová, Lucie, Kautznerová, Dana, Tintěra, Jaroslav, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Saclay, and Institute for Clinical and Experimental Medicine (IKEM)

Subjects
heart diseases, vrozené vady srdce, magnetic, Kardio MR zobrazování, srdce, heart, vrozené vady sr, magnetic resonance examination of heart, získané vady srdce, congenital heart diseases, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, acquired heart diseases, vyšetření srdce magnetickou rezonancí, acquired, cardiovascular system, cardiac MRI, cardiovascular diseases, resonance examination of heart
Abstract

International audience; Early cardiac magnetic resonance imaging (CMR) scans were of inferior quality. A sig- ni cant improvement in image quality was achieved a er introducing the segmented acquisition of the k-space synchronized with patient’s ECG, performed either in breath hold or free breathing. CMR is now used widely in the clinical setting for assessment of cardiac chamber volumes, global and regional cardiac contraction, morphology and tissue charac- terization. In ammatory or post- myocardial infarction changes involve sequences which can appreciate myocardial edema or the use of late post-gadolinium contrast enhancement (LGE) to visualise di use or focal scar. Other applications include the assessment of valvular stenosis or regurgitation by means of ow me- asurement through a de ned ori ce and rst pass myocardial perfusion to assess ischemia. Last but not least, CMR is increasingly used for the assessment of congenital heart diseases, in which CMR o ers objective and directly com- parable measures during serial examinations, without any radiation exposure.

Published
2017

25. Magnetic Resonance Imaging of the Heart and Large Vessels -- Survey of methods and new perspectives

Author

Chabiniok, Radomir, Súkupová, Lucie, Kautznerová, Dana, Tintěra, Jaroslav, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Saclay, and Institute for Clinical and Experimental Medicine (IKEM)

Subjects
heart diseases, vrozené vady srdce, magnetic, Kardio MR zobrazování, srdce, heart, vrozené vady sr, magnetic resonance examination of heart, congenital heart diseases, získané vady srdce, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, acquired heart diseases, vyšetření srdce magnetickou rezonancí, acquired, cardiovascular system, cardiac MRI, cardiovascular diseases, resonance examination of heart
Abstract

International audience; Early cardiac magnetic resonance imaging (CMR) scans were of inferior quality. A sig- ni cant improvement in image quality was achieved a er introducing the segmented acquisition of the k-space synchronized with patient’s ECG, performed either in breath hold or free breathing. CMR is now used widely in the clinical setting for assessment of cardiac chamber volumes, global and regional cardiac contraction, morphology and tissue charac- terization. In ammatory or post- myocardial infarction changes involve sequences which can appreciate myocardial edema or the use of late post-gadolinium contrast enhancement (LGE) to visualise di use or focal scar. Other applications include the assessment of valvular stenosis or regurgitation by means of ow me- asurement through a de ned ori ce and rst pass myocardial perfusion to assess ischemia. Last but not least, CMR is increasingly used for the assessment of congenital heart diseases, in which CMR o ers objective and directly com- parable measures during serial examinations, without any radiation exposure.

Published
2017

26. Relative pressure estimation from velocity measurements in blood flows: state-of-the-art and new approaches

Author

Bertoglio, Cristobal, Núñez, Rodolfo, Galarce, Felipe, Nordsletten, David, Osses, Axel, Center for Mathematical Modeling (CMM), Universidad de Chile = University of Chile [Santiago] (UCHILE), Pontificia Universidad Católica de Valparaíso (PUCV), Imaging Sciences and Biomedical Engineering Division [London], and Guy's and St Thomas' Hospital [London]-King‘s College London

Subjects
Phase-contrast MRI, blood flows, Relative pressure estimation, [SDV]Life Sciences [q-bio], Navier-Stokes equations, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

International audience; The pressure gradient across stenotic blood vessels is an important clinical index for the diagnosis of many cardiovascular diseases. While clinical gold standard for its measurement (in terms of accuracy) is invasive catheterization, Phase-Contrast MR-imaging has emerged as a promising tool for enabling a non-invasive quantification, by linking (highly spatially resolved) velocity measurements with relative pressures via Navier-Stokes equations.In this work we provide a review of current methods for relative pressure estimation, propose new ones and compare them on numerical examples using subsampled and noisy synthetic data. We verify that the newly proposed approaches are more robust with respect to data perturbations and therefore more precise.

Published
2017
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27. Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping

Author

Tom Jackson, Reza Razavi, Jack Lee, Sophie Giffard-Roisin, Lauren Fovargue, Hervé Delingette, Maxime Sermesant, Nicholas Ayache, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, SOFA, European Project: 611823,EC:FP7:ICT,FP7-ICT-2013-10,VP2HF(2013), and European Project: 291080,EC:FP7:ERC,ERC-2011-ADG_20110209,MEDYMA(2012)

Subjects
Computer science, Personalisation, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 0206 medical engineering, Biomedical Engineering, Cardiac electrophysiology, 02 engineering and technology, 030204 cardiovascular system & hematology, Sensitivity and Specificity, Ventricular contraction, 03 medical and health sciences, ECG imaging, 0302 clinical medicine, Text mining, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Heart Conduction System, Patient-Centered Care, medicine, Parameter estimation, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Computer Simulation, Diagnosis, Computer-Assisted, inverse problem of ECG, personalization, Membrane potential, medicine.diagnostic_test, business.industry, Estimation theory, Body Surface Potential Mapping, In- verse Problem of ECG, Models, Cardiovascular, Reproducibility of Results, Torso, 020601 biomedical engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Electrophysiology, medicine.anatomical_structure, ECG Imaging, Cardiac Electrophysiology, Electrical conduction system of the heart, business, parameter estimation, Electrocardiography, Algorithms, Biomedical engineering
Abstract

Selected for the TBME highlights, sept. 2017\url{https://tbme.embs.org/2017/08/24/non-invasive-personalisation-cardiac-electrophysiology-model-body-surface-potential-mapping/}; International audience; Goal: We use non-invasive data (body surface potential mapping, BSPM) to personalise the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then we estimate the main parameters of the cardiac model: activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on 5 non-ischaemic implanted cardiac resynchonisation therapy (CRT) patients with a total of 21 different pacing conditions. Good personalisation results were found in terms of the activation onset location for the PVC (mean distance error, MDE=20.3mm), for the pacing sites (MDE=21.7mm) and for the CRT patients (MDE=24.6mm). We tested the predictive power of the personalised model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalised the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a non-invasive pre-operative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.

Published
2017
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28. Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

Author

Nicholas Ayache, Maxime Sermesant, Jack Lee, Lauren Fovargue, Reza Razavi, Hervé Delingette, Tom Jackson, Sophie Giffard-Roisin, C. Aldo Rinaldi, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mihaela Pop, SOFA, European Project: 611823,EC:FP7:ICT,FP7-ICT-2013-10,VP2HF(2013), and European Project: 291080,EC:FP7:ERC,ERC-2011-ADG_20110209,MEDYMA(2012)

Subjects
Computer science, Personalisation, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 0206 medical engineering, Bayesian probability, 02 engineering and technology, Machine learning, computer.software_genre, 030218 nuclear medicine & medical imaging, Relevance vector machine, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Sinus rhythm, Ground truth, Cardiac electrophysiology, business.industry, Pattern recognition, 020601 biomedical engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Regression, Kernel (statistics), Relevance Vector Machine, ECG Imaging, Artificial intelligence, Cardiac Electrophysiology, business, Nonlinear regression, computer
Abstract

Best paper award FIMH 2017, category: Electrophysiology; International audience; In the scope of modelling cardiac electrophysiology (EP) for understanding pathologies and predicting the response to therapies, patient-specific model parameters need to be estimated. Although per-sonalisation from non-invasive data (body surface potential mapping, BSPM) has been investigated on simple cases mostly with a single pacing site, there is a need for a method able to handle more complex situations such as sinus rhythm with several onsets. In the scope of estimating cardiac activation maps, we propose a sparse Bayesian kernel-based regression (relevance vector machine, RVM) from a large patient-specific simulated database. RVM additionally provides a confidence on the result and an automatic selection of relevant features. With the use of specific BSPM descriptors and a reduced space for the myocardial geometry, we detail this framework on a real case of simultaneous biventricular pacing where both onsets were precisely localised. The obtained results (mean distance to the two ground truth pacing leads is 18.4mm) demonstrate the usefulness of this non-linear approach.

Published
2017
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29. Assessment of atrioventricular valve regurgitation using biomechanical cardiac modeling

Author

Chabiniok, Radomir, Moireau, Philippe, Kiesewetter, Christoph, Hussain, Tarique, Razavi, Reza, Chapelle, Dominique, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Saclay, University of Texas Southwestern Medical Center [Dallas], Pop M and Wright G, Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Inria Saclay - Ile de France, École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
therapy planning, reduced-order model, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB]Life Sciences [q-bio]/Bioengineering, atrioventricular regurgitation, model-based diagnosis assistance, cardiac modeling
Abstract

International audience; In this work we introduce the modeling of atrioventricular valve regurgitation in a spatially reduced order biomechanical heart model. The model can be fast calibrated using non-invasive data of cardiac magnetic resonance imaging and provides an objective measure of contractile properties of the myocardium in the volume overloaded ven-tricle, for which the real systolic function may be masked by the significant level of the atrioventricular valve regurgitation. After demonstrating such diagnostic capabilities, we show the potential of modeling to address some clinical questions concerning possible therapeutic interventions for specific patients. The fast running of the model allows targeting specific questions of referring clinicians in a clinically acceptable time.

Published
2017
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30. ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone

Author

Olaf Dössel, Kawal Rhode, Reza Razavi, YingLiang Ma, Zhong Chen, Rashed Karim, Julian Bostock, Manav Sohal, Martin W. Krueger, Danila Potyagaylo, Anoop Shetty, Christopher A. Rinaldi, Nicholas Ayache, Jatin Relan, Walther H. W. Schulze, Hervé Delingette, Maxime Sermesant, Institute of Biomedical Engineering [Karlsruhe], Karlsruhe Institute of Technology (KIT), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Non contact mapping, Materials science, Heart Ventricles, Scar tissue, Biomedical Engineering, 030204 cardiovascular system & hematology, Ventricular tachycardia, 030218 nuclear medicine & medical imaging, Tikhonov regularization, 03 medical and health sciences, ECG imaging, 0302 clinical medicine, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Non-contact mapping, Monomorphic Ventricular Tachycardia, Body Surface Potential Mapping, Clinical validation, Inverse problem of ECG, Thorax, medicine.disease, Magnetic Resonance Imaging, Computer Science Applications, Grey zone, Catheter Ablation, Tachycardia, Ventricular, Mr images, Electrophysiologic Techniques, Cardiac, Smoothing, Biomedical engineering
Abstract

ECG imaging is an emerging technology for the reconstruction of cardiac electric activity from non-invasively measured body surface potential maps. In this case report, we present the first evaluation of transmurally imaged activation times against endocardially reconstructed isochrones for a case of sustained monomorphic ventricular tachycardia (VT). Computer models of the thorax and whole heart were produced from MR images. A recently published approach was applied to facilitate electrode localization in the catheter laboratory, which allows for the acquisition of body surface potential maps while performing non-contact mapping for the reconstruction of local activation times. ECG imaging was then realized using Tikhonov regularization with spatio-temporal smoothing as proposed by Huiskamp and Greensite and further with the spline-based approach by Erem et al. Activation times were computed from transmurally reconstructed transmembrane voltages. The results showed good qualitative agreement between the non-invasively and invasively reconstructed activation times. Also, low amplitudes in the imaged transmembrane voltages were found to correlate with volumes of scar and grey zone in delayed gadolinium enhancement cardiac MR. The study underlines the ability of ECG imaging to produce activation times of ventricular electric activity-and to represent effects of scar tissue in the imaged transmembrane voltages.

Published
2017
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31. Analysis of passive cardiac constitutive laws for parameter estimation using 3D tagged MRI

Author

Reza Razavi, Liya Asner, Myrianthi Hadjicharalambous, Gerald Carr-White, Eva Sammut, Jack Lee, Nicolas P. Smith, David Nordsletten, Radomir Chabiniok, James Wong, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Faculty of Engineering [Auckland], University of Auckland [Auckland], École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
Adult, Male, Engineering, Relation (database), [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, media_common.quotation_subject, 0206 medical engineering, Constitutive equation, Fidelity, 02 engineering and technology, Parameter space, Displacement (vector), 3D tagged MRI, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Imaging, Three-Dimensional, 0302 clinical medicine, Diastole, Modelling and Simulation, Parameter estimation, Pressure, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Humans, Practical identifiability, media_common, Original Paper, Patient-specific modeling, Ground truth, business.industry, Estimation theory, Mechanical Engineering, Models, Cardiovascular, Heart, Magnetic Resonance Imaging, 020601 biomedical engineering, Cardiac mechanics, Modeling and Simulation, Law, Identifiability, Constitutive laws, business, Biotechnology
Abstract

International audience; An unresolved issue in patient-specific models of cardiac mechanics is the choice of an appropriate constitutive law, able to accurately capture the passive behavior of the myocardium, while still having uniquely identifiable parameters tunable from available clinical data. In this paper, we aim to facilitate this choice by examining the practical identifiability and model fidelity of constitutive laws often used in cardiac mechanics. Our analysis focuses on the use of novel 3D tagged MRI, providing detailed displacement information in three dimensions. The practical identifiability of each law is examined by generating synthetic 3D tags from in silico simulations, allowing mapping of the objective function landscape over parameter space and comparison of minimizing parameter values with original ground truth values. Model fidelity was tested by comparing these laws with the more complex transversely isotropic Guccione law, by characterizing their passive end-diastolic pressure–volume relation behavior, as well as by considering the in vivo case of a healthy volunteer. These results show that a reduced form of the Holzapfel–Ogden law provides the best balance between identifiability and model fidelity across the tests considered.

Published
2014
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32. Long-term Survival and Risk Analysis in 136 Consecutive Patients With Type B Aortic Dissection Presenting to a Single Centre Over an 11 Year Period

Author

Claire Mounier Vehier, Stéphan Haulon, Janet L. Peacock, Rachel E. Clough, Pascal Delsart, Rafaelle Spear, Jonathan Sobocinski, Guillaume Ledieu, David Barillà, Siobhan Crichton, Imaging Sciences and Biomedical Engineering Division [London], and Guy's and St Thomas' Hospital [London]-King‘s College London

Subjects
Male, Time Factors, Survival, Risk analysis, Computed Tomography Angiography, [SDV]Life Sciences [q-bio], Aortic dissection, Kaplan-Meier Estimate, 030230 surgery, 030204 cardiovascular system & hematology, Risk Assessment, 03 medical and health sciences, Postoperative Complications, 0302 clinical medicine, Risk Factors, Long term survival, Medicine, Humans, In patient, 030212 general & internal medicine, Endovascular treatment, Antihypertensive Agents, Aged, Proportional Hazards Models, Retrospective Studies, Aortic Aneurysm, Thoracic, Proportional hazards model, business.industry, Type B aortic dissection, Incidence (epidemiology), Endovascular Procedures, Middle Aged, medicine.disease, Progression-Free Survival, 3. Good health, Single centre, Aortic Dissection, Risk analysis (engineering), cardiovascular system, Female, Surgery, business, Cardiology and Cardiovascular Medicine, Follow-Up Studies
Abstract

Objectives To evaluate, in patients with acute type B aortic dissection, the results of medical and endovascular treatment in a large single centre experience and to investigate the clinical and imaging features on presentation that relate to poor outcome. Methods This was a retrospective analysis of prospectively collected clinical and CT imaging data. Consecutive patients (136) with acute type B aortic dissection were included in the study over an 11 year period. The characteristics of patients receiving endovascular (complicated) or medical treatment (uncomplicated) were compared. Kaplan–Meier estimators were used to estimate cumulative overall survival and survival free of aortic events. Factors associated with overall and aortic event free survival were also explored using Cox proportional hazards models. Results The mean follow up was 51 months (1–132), during which time 33 deaths and 48 aortic events occurred. At one and five years, overall survival was 94.0% and 74.8%, respectively, and freedom from aortic events was 75.6% and 58.7%. There was no difference in all cause survival and aortic event free survival at one and five years between the patients treated endovascularly and those receiving medical treatment alone. Risk analysis for aortic events demonstrated the maximum size of the proximal entry tear, the maximum thoracic aortic diameter, and the thoracic aortic false lumen maximum diameter to have a significant effect on the incidence of aortic events. Conclusions Active management of patients with type B aortic dissection results in good long-term survival even in the presence of features traditionally associated with adverse outcomes. All patients require close lifetime surveillance as aortic events continue to occur during follow up even after endografting.

Published
2019
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33. Estimation of Purkinje Activation from ECG: an Intermittent Left Bundle Branch Block Study

Author

Nicholas Ayache, Reza Razavi, Jessica Webb, Jack Lee, Roch Molléro, Hervé Delingette, Maxime Sermesant, Sophie Giffard-Roisin, Lauren Fovargue, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, SOFA, European Project: 611823,EC:FP7:ICT,FP7-ICT-2013-10,VP2HF(2013), and European Project: 291080,EC:FP7:ERC,ERC-2011-ADG_20110209,MEDYMA(2012)

Subjects
0301 basic medicine, Purkinje System, Computer science, business.industry, Electrophysiological Model, Forward EP Model, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Parameter Estimation, 030204 cardiovascular system & hematology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Electrophysiology, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Artificial intelligence, business
Abstract

In press; International audience; Modelling the cardiac electrophysiology (EP) can help understand pathologies and predict the response to therapies such as cardiac resynchronization. To this end, estimating patient-specific model parameters is crucial. In the case of patients with bundle branch blocks (BBB), part of the Purkinje system is often affected. The aim of this work is to estimate the activation of the right and left Purkinje systems from standard non-invasive techniques: magnetic resonance imaging (MRI) and 12-lead electrocardiogram (ECG). As it is difficult to differentiate the contribution of the Purkinje system, this work relies on a particular intermittent left BBB (LBBB) case where both LBBB and absence of LBBB (ALBBB) were recorded on different 12-lead ECGs. First, an efficient forward EP model is proposed by coupling a Mitchell-Schaeffer cardiac model with a current dipole formulation that simulates the ECG. We used the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm to optimize the 3 parameters by minimizing the error with the real ECG. The estimation of conduction velocity (CV) parameters for LBBB and ALBBB shows a good agreement on the myocardial CV (0.39m/s for ABBB, 0.40m/s for LBBB), while the estimation of the left Purkinje CV seems to identify the pathology (1.32m/s for ALBBB, 0.49m/s for LBBB). Finally, the plots of the simulated 12-lead ECGs together with the ground truth ECGs indicate similar shapes.

Published
2016

34. Non-invasive Model-Based Assessment of Passive Left-Ventricular Myocardial Stiffness in Healthy Subjects and in Patients with Non-ischemic Dilated Cardiomyopathy

Author

Liya Asner, Nicolas P. Smith, Devis Peressutti, Andrew P. King, Eva Sammut, Radomir Chabiniok, Reza Razavi, Jack Lee, Eric Kerfoot, Gerald Carr-White, James Wong, Myrianthi Hadjicharalambous, David Nordsletten, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Inria Saclay - Ile de France, École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
Cardiomyopathy, Dilated/diagnostic imaging, Adult, Cardiomyopathy, Dilated, Male, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Patient-specific modelling, Heart Ventricles, 0206 medical engineering, Biomedical Engineering, Myocardial stiffness, 02 engineering and technology, Heart Ventricles/diagnostic imaging, Article, 030218 nuclear medicine & medical imaging, Stiffness, 03 medical and health sciences, 0302 clinical medicine, Pericardium/diagnostic imaging, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Parameter uniqueness, medicine, Humans, In patient, Precision Medicine, business.industry, Myocardium, Non invasive, Healthy subjects, Models, Cardiovascular, Dilated cardiomyopathy, Precision Medicine/methods, Middle Aged, medicine.disease, 020601 biomedical engineering, Model uncertainties, Identifiability, Female, Non ischemic, medicine.symptom, business, Pericardium, Biomedical engineering
Abstract

Patient-specific modelling has emerged as a tool for studying heart function, demonstrating the potential to provide non-invasive estimates of tissue passive stiffness. However, reliable use of model-derived stiffness requires sufficient model accuracy and unique estimation of model parameters. In this paper we present personalised models of cardiac mechanics, focusing on improving model accuracy, while ensuring unique parametrisation. The influence of principal model uncertainties on accuracy and parameter identifiability was systematically assessed in a group of patients with dilated cardiomyopathy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=3$$\end{document}n=3) and healthy volunteers (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=5$$\end{document}n=5). For all cases, we examined three circumferentially symmetric fibre distributions and two epicardial boundary conditions. Our results demonstrated the ability of data-derived boundary conditions to improve model accuracy and highlighted the influence of the assumed fibre distribution on both model fidelity and stiffness estimates. The model personalisation pipeline—based strictly on non-invasive data—produced unique parameter estimates and satisfactory model errors for all cases, supporting the selected model assumptions. The thorough analysis performed enabled the comparison of passive parameters between volunteers and dilated cardiomyopathy patients, illustrating elevated stiffness in diseased hearts. Electronic supplementary material The online version of this article (doi:10.1007/s10439-016-1721-4) contains supplementary material, which is available to authorized users.

Published
2016
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35. Eidolon: Visualization and Computational Framework for Multi-Modal Biomedical Data Analysis

Author

Kerfoot, Eric, Fovargue, Lauren, Rivolo, Simone, Shi, Wenzhe, Rueckert, Daniel, Nordsletten, David, Lee, Jack, Chabiniok, Radomir, Razavi, Reza, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing , and quantitative analysis. Medical imaging provides rich information , from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis , showing the Eidolon supports and enhances the workflow.

Published
2016

36. Finite Element Digital Image Correlation for Cardiac Strain Analysis from 3D Whole-Heart Tagging

Author

Genet, Martin, Stoeck, Christian, von Deuster, Constantin, Lee, Lik Chuan, Guccione, Julius, Kozerke, Sebastian, Institute for Biomedical Engineering [ETH Zürich] (IBT), Universität Zürich [Zürich] = University of Zurich (UZH)-Department of Information Technology and Electrical Engineering [Zürich] (D-ITET), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Michigan State University [East Lansing], Michigan State University System, Department of Surgery, University of California, San Francisco, CA, USA (UCSF), and UCSF

Subjects
[SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Whole-heart 3D CSPAMM allows for fast and reliable functional imaging of the beating heart by mapping the deformation of magnetization patterns throughout the left ventricle and cardiac cycle [1]. Its clinical use has been limited mainly due to (i) the time-consuming post-processing of tagged MR images, and (ii) derivation of clinically relevant biomarkers from the data. Regarding the post-processing of tagged MR images, multiple approaches have been proposed [2]; however, some of these methods are computationally expensive and consensus about a preferred algorithm is still lacking. Regarding clinically relevant prognostic information, biomechanical models are increasingly considered as a potential tool to characterize patient condition and predict patient outcome through the integration of patient-specific data including ventricular strain [3]–[5]; however, the integration of finite element biomechanical models and image data remains challenging. The objective of the present work was to develop, validate and analyze a finite element digital image correlation tool to extract ventricular strain data from MR images, which can be applied to both 3D CSPAMM images and conventional multi-slice cine images.

Published
2016

37. Age-related changes in intraventricular kinetic energy: a physiological or pathological adaptation?

Author

Wong, James, Chabiniok, Radomir, deVecchi, Adelaide, Dedieu, Nathalie, Sammut, Eva, Schaeffter, Tobias, Razavi, Reza, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris

Subjects
Adult, Male, Aging, Adolescent, Systole, Magnetic Resonance Imaging, Cine, heart failure, Ventricular Function, Left, Adaptation, Physiological/physiology, cardiovascular magnetic resonance, Ventricular Dysfunction, Left, Young Adult, Integrative Cardiovascular Physiology and Pathophysiology, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Diastole, Humans, Prospective Studies, Ventricular Dysfunction, Left/physiopathology, Child, Aged, Blood Flow Velocity/physiology, Age Factors, Infant, Stroke Volume, Middle Aged, Adaptation, Physiological, Aging/physiology, four-dimensional phase-contrast magnetic resonance imaging, Kinetics, Stroke Volume/physiology, Case-Control Studies, Child, Preschool, cardiovascular system, cardio-vascular magnetic resonance, Female, Ventricular Function, Left/physiology, cardiac function, Blood Flow Velocity, energy
Abstract

Measuring intracardiac kinetic energy using four-dimensionl flow cardiac magnetic resonance provides important information on the decline in the early diastolic kinetic energy of blood with aging. The decline is comparable with that seen in those with heart failure and may be a marker of cardiac function., Aging has important deleterious effects on the cardiovascular system. We sought to compare intraventricular kinetic energy (KE) in healthy subjects of varying ages with subjects with ventricular dysfunction to understand if changes in energetic momentum may predispose individuals to heart failure. Four-dimensional flow MRI was acquired in 35 healthy subjects (age: 1–67 yr) and 10 patients with left ventricular (LV) dysfunction (age: 28–79 yr). Healthy subjects were divided into age quartiles (1st quartile

Published
2016
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38. Sample Shuttling Relaxometry of Contrast Agents: NMRD Profiles above 1 T with a Single Device

Author

Thorsten Marquardsen, Yves Gossuin, Zeinab Serhan, Rafael Torres Martin de Rosales, Lydia Sandiford, Fabien Ferrage, Daniel Henrard, Dimitrios Sakellariou, Biomedical Physics Department, Université Pierre et Marie Curie - Paris 6 (UPMC), Département de Chimie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Bruker Biospin GmbH, bruker, Laboratoire Structure et Dynamique par Résonance Magnétique (LCF) (LSDRM), Nanosciences et Innovation pour les Matériaux, la Biomédecine et l'Energie (ex SIS2M) (NIMBE UMR 3685), Institut Rayonnement Matière de Saclay (IRAMIS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Institut Rayonnement Matière de Saclay (IRAMIS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), European Project: 279519,EC:FP7:ERC,ERC-2011-StG_20101014,2F4BIODYN(2012), École normale supérieure - Paris (ENS-PSL), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut Rayonnement Matière de Saclay (IRAMIS), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Larmor precession, Relaxometry, Gadolinium, Relaxation (NMR), Spin–lattice relaxation, chemistry.chemical_element, 02 engineering and technology, 010402 general chemistry, 021001 nanoscience & nanotechnology, equipment and supplies, 01 natural sciences, Article, Atomic and Molecular Physics, and Optics, 0104 chemical sciences, Paramagnetism, Nuclear magnetic resonance, chemistry, Magnet, Magnetic nanoparticles, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [PHYS.PHYS.PHYS-CHEM-PH]Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph], 0210 nano-technology, human activities
Abstract

Nuclear magnetic relaxation dispersion (NMRD) profiles are essential tools to evaluate the efficiency and investigate the properties of magnetic compounds used as contrast agents for magnetic resonance imaging (MRI), namely gadolinium chelates and superparamagnetic iron oxide particles. These curves represent the evolution of proton relaxation rates with the magnetic field. NMRD profiles are unparalleled to probe extensively the spectral density function involved in the relaxation of water in the presence of the paramagnetic ion or the magnetic nanoparticles. This makes such profiles an excellent test of the adequacy of a theoretical relaxation model and allow for a predictive approach to the development and optimization of contrast agents. From a practical point of view they also allow to evaluate the efficiency of a contrast agent in a certain range of magnetic fields. Nowadays, these curves are recorded with commercial fast field cycling devices, often limited to a maximum Larmor frequency of 40 MHz (0.94 T). In this article, relaxation data were acquired on a wide range of magnetic fields, from 3.5 × 10−4 to 14 T, for a gadolinium-based contrast agent and for PEGylated iron oxide nanoparticles. We show that the low-field NMRD curves can be completed with high-field data obtained on a shuttle apparatus device using the superconductive magnet of a high-field spectrometer. This allows a better characterization of the contrast agents at relevant magnetic fields for clinical and preclinical MRI, but also refines the experimental data that could be used for the validation of relaxation models. Electronic supplementary material The online version of this article (doi:10.1007/s00723-015-0751-7) contains supplementary material, which is available to authorized users.

Published
2016
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39. Second-order motion-compensated spin echo diffusion tensor imaging of the human heart

Author

Stoeck, Christian T., von Deuster, Constantin, Genet, Martin, Atkinson, David, Kozerke, Sebastian, University of Zurich, Kozerke, Sebastian, Institute for Biomedical Engineering [ETH Zürich] (IBT), Universität Zürich [Zürich] = University of Zurich (UZH)-Department of Information Technology and Electrical Engineering [Zürich] (D-ITET), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, and Centre for Medical Imaging, University College London, London, United Kingdom

Subjects
170 Ethics, in vivo cardiac DTI, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, myocardial microstructure, 2741 Radiology, Nuclear Medicine and Imaging, 610 Medicine & health, 10237 Institute of Biomedical Engineering, diffusion tensor imaging, spin, echo
Abstract

International audience; Myocardial microstructure has been challenging to probe in vivo. Spin echo-based diffusion-weighted sequences allow for single-shot acquisitions but are highly sensitive to cardiac motion. In this study, the use of second-order motion-compensated diffusion encoding was compared with first-order motion-compensated diffusion-weighted imaging during systolic contraction of the heart.First- and second-order motion-compensated diffusion encoding gradients were incorporated into a triggered single-shot spin echo sequence. The effect of contractile motion on the apparent diffusion coefficients and tensor orientations was investigated in vivo from basal to apical level of the heart.Second-order motion compensation was found to increase the range of systolic trigger delays from 30%-55% to 15%-77% peak systole at the apex and from 25%-50% to 15%-79% peak systole at the base. Diffusion tensor analysis yielded more physiological transmural distributions when using second-order motion-compensated diffusion tensor imaging.Higher-order motion-compensated diffusion encoding decreases the sensitivity to cardiac motion, thereby enabling cardiac DTI over a wider range of time points during systolic contraction of the heart.

Published
2016
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40. Sports-Related Sudden Death in the General Population

Author

Eloi Marijon, Jean-Philippe Empana, David S. Celermajer, Michel Rieu, Hazrije Mustafic, Michel Desnos, Jean-Yves Le Heuzey, Marie-Cécile Perier, Florence Dumas, Nordine Benameur, Muriel Tafflet, Xavier Jouven, Jean-François Toussaint, Paris-Centre de Recherche Cardiovasculaire (PARCC - UMR-S U970), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Institut de recherche biomédicale et d’épidémiologie du sport (IRMES - EA 7329), Université Paris Descartes - Paris 5 (UPD5)-Institut national du sport, de l'expertise et de la performance (INSEP), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Université Paris Descartes - Paris 5 (UPD5), Therapie Cellulaire en Pathologie Cardio-Vasculaire (UMR_S 633), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Agence Française de Lutte contre le Dopage (AFLD), INSEP, documentation, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5)

Subjects
Male, Survival, medicine.medical_treatment, bystander help, Poison control, cardiac arrest, 030204 cardiovascular system & hematology, 0302 clinical medicine, [SHS.SPORT.SS]Humanities and Social Sciences/Sport/Sport heath, [SHS.SPORT.SS] Humanities and Social Sciences/Sport/Sport heath, Prospective Studies, 030212 general & internal medicine, Child, physical, education.field_of_study, exercise, Data Collection, Incidence, Incidence (epidemiology), Age Factors, Middle Aged, defibrillation, 3. Good health, Female, France, Cardiology and Cardiovascular Medicine, Sports, Adult, Chest Pain, medicine.medical_specialty, Adolescent, Population, Electric Countershock, Context (language use), arrhythmia, cardiopulmonary resuscitation, Sudden death, Young Adult, 03 medical and health sciences, Physiology (medical), medicine, First Aid, Humans, Cardiopulmonary resuscitation, education, Aged, business.industry, Arrhythmias, Cardiac, Odds ratio, Confidence interval, Heart Arrest, Surgery, Death, Sudden, Cardiac, athletes, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Emergency medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business
Abstract

Background— Although such data are available for young competitive athletes, the prevalence, characteristics, and outcome of sports-related sudden death have not been assessed previously in the general population. Methods and Results— A prospective and comprehensive national survey was performed throughout France from 2005 to 2010, involving subjects 10 to 75 years of age. Case detection for sports-related sudden death, including resuscitated cardiac arrest, was undertaken via national ambulance service reporting and Web-based screening of media releases. The overall burden of sports-related sudden death was 4.6 cases per million population per year, with 6% of cases occurring in young competitive athletes. Sensitivity analyses used to address suspected underreporting demonstrated an incidence ranging from 5 to 17 new cases per million population per year. More than 90% of cases occurred in the context of recreational sports. The age of subjects was relatively young (mean±SD 46±15 years), with a predominance of men (95%). Although most cases were witnessed (93%), bystander cardiopulmonary resuscitation was only commenced in 30.7% of cases. Bystander cardiopulmonary resuscitation (odds ratio 3.73, 95% confidence interval 2.19 to 6.39, P P Conclusions— Sports-related sudden death in the general population is considerably more common than previously suspected. Most cases are witnessed, yet bystander cardiopulmonary resuscitation was only initiated in one third of cases. Given the often predictable setting of sports-related sudden death and that prompt interventions were significantly associated with improved survival, these data have implications for health services planning.

Published
2011
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41. euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling

Author

Walther H. W. Schulze, Jatin Relan, Dominique Chapelle, Matt McCormick, Helko Lehmann, Philipp Beerbaum, Maxime Sermesant, David Nordsletten, Jos A. E. Spaan, Alejandro F. Frangi, Israel Valverde, Peter Hunter, Reza Rezavi, Rod Hose, Nicholas Ayache, Juergen Weese, Cristina Staicu, Maria Siebes, Hervé Delingette, Oscar Camara, Nic Smith, Adelaide de Vecchi, Martin W. Krueger, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Computing Laboratory (OUCL), University of Oxford, Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra [Barcelona] (UPF), Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III [Madrid] (ISC)-ministerio de ciencia e innovacion, Institució Catalana de Recerca i Estudis Avançats (ICREA), Institute of Simulation and Graphics [Magdeburg], Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute of Biomedical Engineering [Karlsruhe], Karlsruhe Institute of Technology (KIT), Department of Cardiovascular Science [Sheffield], University of Sheffield [Sheffield], Academic Medical Center - Academisch Medisch Centrum [Amsterdam] (AMC), University of Amsterdam [Amsterdam] (UvA), Auckland Bioengineering Institute, University of Auckland [Auckland], Philips Research Laboratories [Eindhoven], Philips Research [Nederlands], Philips Research, Modeling, analysis and control in computational structural dynamics (MACS), Inria Paris-Rocquencourt, Division of Imaging Sciences, King‘s College London, NIHR Biomedical Research Centre [London], Guy's and St Thomas' NHS Foundation Trust-King‘s College London, University of Oxford [Oxford], Otto-von-Guericke University [Magdeburg] (OVGU), ACS - Amsterdam Cardiovascular Sciences, Biomedical Engineering and Physics, and Other Research

Subjects
Cardiac function curve, medicine.medical_specialty, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 0206 medical engineering, Population, Biomedical Engineering, Biophysics, Pump function, Bioengineering, 02 engineering and technology, Disease, 030204 cardiovascular system & hematology, Bioinformatics, Biochemistry, Biomaterials, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Medicine, Clinical imaging, education, Intensive care medicine, education.field_of_study, business.industry, Virtual Physiological Human, Articles, Patient specific, 020601 biomedical engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 3. Good health, Lifetime risk, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Biotechnology
Abstract

International audience; The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.

Published
2011
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42. Estimation of passive and active properties in the human heart using 3D tagged MRI

Author

Reza Razavi, Gerald Carr-White, Radomir Chabiniok, Philip Chowienczyk, Devis Peresutti, Eva Sammut, Andrew P. King, David Nordsletten, Myrianthi Hadjicharalambous, Jack Lee, Nicolas P. Smith, Liya Asner, James Wong, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of cardiology [Guy's and St. Thomas ' hospitals] [London], Guy's and St Thomas' Hospital [London]-Guy's Hospital [London], Department of Clinical Pharmacology [London], Faculty of Engineering [Auckland], University of Auckland [Auckland], École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
Computer science, Systole, Patient-specific modelling, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Pipeline (computing), 0206 medical engineering, Image processing, 02 engineering and technology, Kinematics, computer.software_genre, 030218 nuclear medicine & medical imaging, Magnetic Resonance Imaging/methods, 3D tagged MRI, 03 medical and health sciences, 0302 clinical medicine, Imaging, Three-Dimensional, Modelling and Simulation, Medical imaging, Parameter estimation, Humans, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Computer Simulation, Original Paper, Estimation theory, Mechanical Engineering, Model selection, Systole/physiology, Heart, 020601 biomedical engineering, Magnetic Resonance Imaging, Heart/physiology, Cardiac mechanics, Modeling and Simulation, Identifiability, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Data mining, Noise (video), computer, Biotechnology
Abstract

International audience; Advances in medical imaging and image processing are paving the way for personalised cardiac biomechani-cal modelling. Models provide the capacity to relate kinematics to dynamics and—through patient-specific modelling— derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechan-ical analysis.

Published
2015
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43. Spin echo versus stimulated echo diffusion tensor imaging of the in vivo human heart

Author

von Deuster, Constantin, Stoeck, Christian T, Genet, Martin, Atkinson, David, Kozerke, Sebastian, Institute for Biomedical Engineering [ETH Zürich] (IBT), Universität Zürich [Zürich] = University of Zurich (UZH)-Department of Information Technology and Electrical Engineering [Zürich] (D-ITET), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Centre for Medical Imaging, University College London, London, United Kingdom, University of Zurich, and Kozerke, Sebastian

Subjects
signal‐to‐noise ratio, Cardiac-Gated Imaging Techniques, Magnetic Resonance Imaging, Cine, 610 Medicine & health, spin‐echo, motion compensation, Signal-To-Noise Ratio, Sensitivity and Specificity, 170 Ethics, Myocardial fiber architecture, Image Interpretation, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 2741 Radiology, Nuclear Medicine and Imaging, Spin-echo, motion compensation, Humans, 10237 Institute of Biomedical Engineering, Cardiac diffusion tensor imaging, Imaging Methodology—Full Papers, ComputingMilieux_MISCELLANEOUS, STEAM, Full Paper, Echo-Planar Imaging, Phantoms, Imaging, Reproducibility of Results, Heart, Image Enhancement, Diffusion Tensor Imaging, nervous system, Radiology Nuclear Medicine and imaging, cardiac diffusion tensor imaging, myocardial fiber architecture, Signal-to-noise ratio, Spin Labels, Algorithms
Abstract

Purpose To compare signal‐to‐noise ratio (SNR) efficiency and diffusion tensor metrics of cardiac diffusion tensor mapping using acceleration‐compensated spin‐echo (SE) and stimulated echo acquisition mode (STEAM) imaging. Methods Diffusion weighted SE and STEAM sequences were implemented on a clinical 1.5 Tesla MR system. The SNR efficiency of SE and STEAM was measured (b = 50–450 s/mm2) in isotropic agar, anisotropic diffusion phantoms and the in vivo human heart. Diffusion tensor analysis was performed on mean diffusivity, fractional anisotropy, helix and transverse angles. Results In the isotropic phantom, the ratio of SNR efficiency for SE versus STEAM, SNRt(SE/STEAM), was 2.84 ± 0.08 for all tested b‐values. In the anisotropic diffusion phantom the ratio decreased from 2.75 ± 0.05 to 2.20 ± 0.13 with increasing b‐value, similar to the in vivo decrease from 2.91 ± 0.43 to 2.30 ± 0.30. Diffusion tensor analysis revealed reduced deviation of helix angles from a linear transmural model and reduced transverse angle standard deviation for SE compared with STEAM. Mean diffusivity and fractional anisotropy were measured to be statistically different (P < 0.001) between SE and STEAM. Conclusion Cardiac DTI using motion‐compensated SE yields a 2.3–2.9× increase in SNR efficiency relative to STEAM and improved accuracy of tensor metrics. The SE method hence presents an attractive alternative to STEAM based approaches. Magn Reson Med 76:862–872, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Published
2015
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44. Bridging Three Orders of Magnitude: Multiple Scattered Waves Sense Fractal Microscopic Structures via Dispersion

Author

Sverre Holm, Lynne E. Bilston, Bojan B. Guzina, Christian Michler, Simon A. Lambert, Ralph Sinkus, Lauriane Jugé, Sven Peter Näsholm, Jean Michel Serfaty, David Nordsletten, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Télécom ParisTech, Hémostase, bio-ingénierie et remodelage cardiovasculaires (LBPC), Université Paris Diderot - Paris 7 (UPD7)-Université Paris 13 (UP13)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Galilée, University of Minnesota [Twin Cities] (UMN), University of Minnesota System, Physique des ondes pour la médecine, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Physics, Shear waves, business.industry, Scattering, General Physics and Astronomy, Models, Theoretical, Wavelength, Superposition principle, Fractals, Sound, Optics, Orders of magnitude (time), Macroscopic scale, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer Simulation, [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], Phase velocity, business, Dispersion (water waves), ComputingMilieux_MISCELLANEOUS
Abstract

Wave scattering provides profound insight into the structure of matter. Typically, the ability to sense microstructure is determined by the ratio of scatterer size to probing wavelength. Here, we address the question of whether macroscopic waves can report back the presence and distribution of microscopic scatterers despite several orders of magnitude difference in scale between wavelength and scatterer size. In our analysis, monosized hard scatterers 5 μm in radius are immersed in lossless gelatin phantoms to investigate the effect of multiple reflections on the propagation of shear waves with millimeter wavelength. Steady-state monochromatic waves are imaged in situ via magnetic resonance imaging, enabling quantification of the phase velocity at a voxel size big enough to contain thousands of individual scatterers, but small enough to resolve the wavelength. We show in theory, experiments, and simulations that the resulting coherent superposition of multiple reflections gives rise to power-law dispersion at the macroscopic scale if the scatterer distribution exhibits apparent fractality over an effective length scale that is comparable to the probing wavelength. Since apparent fractality is naturally present in any random medium, microstructure can thereby leave its fingerprint on the macroscopically quantifiable power-law exponent. Our results are generic to wave phenomena and carry great potential for sensing microstructure that exhibits intrinsic fractality, such as, for instance, vasculature.

Published
2015
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45. A whole heart model with finite growth for hypertension-induced pathologies

Author

Genet, Martin, Lee, Lik Chuan, Toutain, Arnaud, Baillargeon, Brian, Kozerke, Sebastian, Guccione, Julius, Kuhl, Ellen, Department of Medicine [San Francisco], University of California [San Francisco] (UCSF), University of California-University of California, Michigan State University [East Lansing], Michigan State University System, Dassault Systèmes Simulia Corporation, Fremont, CA, USA, Institute for Biomedical Engineering [ETH Zürich] (IBT), Universität Zürich [Zürich] = University of Zurich (UZH)-Department of Information Technology and Electrical Engineering [Zürich] (D-ITET), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, and Stanford University

Subjects
[SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], Finite Growth, Computational Cardiac Mechanics, Whole Heart Modeling
Abstract

International audience; Heart failure (HF) is a leading cause of morbidity and has a 5-year mortality rate of 50%. Up to 70% of all HF conditions are of ischemic origin and all systolic HF conditions involve some degree of myocardial ischemia, in which there is a mismatch between coronary blood supply and myocardial demand. The incidence of advanced HF continues to increase despite considerable progress in medical management and the more recent development of devices for treating HF. For end-stage HF, heart transplantation or mechanical assist devices are the only viable options. Although current devices and therapies are useful, early diagnosis is most effective for long-term outcome. Clearly, early diagnosis and prediction of propensity to HF can stratify susceptible patients for preventative medical therapy, including treatment of co-morbidities, to combat this health care epidemic [1]. In this work, we investigate the role of multiple growth mechanisms in disease progression and ventricular mechanics. More precisely, we study longitudinal and transverse growth in systemic and pulmonary hypertension-induced hypertrophy. For this, we use the finite growth theory [2], [3], and the Living Heart Project's whole heart model [4].

Published
2015

46. Patient-Specific Biomechanical Modeling of Cardiac Amyloidosis – A Case Study

Author

Aziz Guellich, Radomir Chabiniok, Jean-François Deux, Dominique Chapelle, Thibaud Damy, Alessandro Felder, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Unité fonctionnelle insuffisance cardiaque, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor-Université Paris-Est (UPE), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), H. van Assen, P. Bovendeerd, T. Delhaas, École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
amyloidosis, medicine.medical_specialty, Heartbeat, business.industry, Amyloidosis, heart failure, Patient specific, medicine.disease, Surgery, cardiac modeling, Physical medicine and rehabilitation, Cardiac amyloidosis, patient-specific, Heart failure, medicine, business, Passive stiffness, Venous return curve, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

International audience; We present a patient-specific biomechanical modeling framework and an initial case study for investigating cardiac amyloidosis (CA). Our patient-specific heartbeat simulations are in good agreement with the data, and our model calibration indicates that the major effect of CA in the biophysical behavior lies in a dramatic increase of the passive stiffness. We also conducted a preliminary trial for predicting the effects of pharmacological treatments – which is an important clinical challenge – based on the model combined with a simple venous return representation. This requires further investigation and validation, albeit provides some valuable preliminary insight.

Published
2015
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47. Steps Towards Quantification of the Cardiological Stress Exam

Author

Eva Sammut, Reza Razavi, Radomir Chabiniok, Myrianthi Hadjicharalambous, David Nordsletten, Nicolas P. Smith, Liya Asner, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
Short axis, business.industry, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 0206 medical engineering, 02 engineering and technology, Dobutamine stress, 020601 biomedical engineering, 01 natural sciences, Acquisition Protocol, 010101 applied mathematics, Stress (mechanics), Contractility, Quantitative assessment, medicine, Dobutamine, [SDV.IB]Life Sciences [q-bio]/Bioengineering, 0101 mathematics, business, Image resolution, Simulation, medicine.drug, Biomedical engineering
Abstract

International audience; In this work we aim to advance the translation of model-based myocardial contractility estimation to the clinical problem of quantitative assessment of the dobutamine stress exam. In particular, we address the question of limited spatial resolution of the observations obtained from cine MRI during the stress test, in which typically only a small number of cine MRI slices are acquired. Due to the relative risk during the dobutamine infusion, a safe acquisition protocol with a healthy volunteer under the infusion of a beta-blocker is applied in order to get a better insight into the contractility estimation using such a type of clinical data. The estimator is compared for three types of observations, namely the processed short axis cine stack contiguously covering the ven-tricles, the short axis stack limited to only 3 slices and the combination of 3 short and 3 long axis slices. A decrease of contractilities in AHA regions under the beta-blocker infusion was estimated for each observation. The corrected model (by using the estimated parameters) was then compared with the displacements extracted from 3D tagged MRI.

Published
2015
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48. 3D myofiber reconstruction from in vivo cardiac DTI data through extraction of low rank modes

Author

Genet, Martin, von Deuster, Constantin, Stoeck, Christian T., Kozerke, Sebastian, Institute for Biomedical Engineering [ETH Zürich] (IBT), Universität Zürich [Zürich] = University of Zurich (UZH)-Department of Information Technology and Electrical Engineering [Zürich] (D-ITET), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Imaging Sciences and Biomedical Engineering Division [London], and Guy's and St Thomas' Hospital [London]-King‘s College London

Subjects
[SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph]
Abstract

International audience; Recent advances in cardiac diffusion tensor imaging (DTI) have enabled robust imaging of the in-vivo human heart [1,2]. This allows to non-invasively assess myofiber and myosheet orientations, which has been a long standing bottleneck of patient-specific cardiac modeling [3]. However, in view of the relatively low scan efficiency and scan time constraints in-vivo, cardiac coverage and signal-to-noise ratio are limited. To this end, data extrapolation [4] and noise reduction [5] techniques have been proposed. The objectives of the present work are to propose an approach to (i) extract the most significant features from noisy myofiber and myosheet angle maps measured with in-vivo DTI, and (ii) extrapolate the data across the entire left-ventricle based on a limited number of acquired short-axis views.

Published
2015

49. Combining multi-parametric MR images for the detection of epileptogenic lesions

Author

Julien Jung, Meriem El Azami, Carole Lartizien, Romain Bouet, Alexander Hammers, 2 - Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), Centre de recherche en neurosciences de Lyon ( CRNL ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Fondation neurodis, Fondation Neurodis, Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche en neurosciences de Lyon (CRNL), and Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Multi parametric, Computer science, business.industry, Feature extraction, Univariate, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, medicine.disease, Cross-validation, Epilepsy, medicine, Brain lesions, Anomaly detection, In patient, Computer vision, Artificial intelligence, Mr images, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Diffusion MRI
Abstract

International audience; Detecting epileptogenic brain lesions in patients with intractable epilepsy is important as patients may then be eligible for surgery. We compared two methods of anomaly detection via combining multi-parametric MR images. The early fusion approach consists in building a single global model; in the late fusion approach three local models are constructed based each on a single MR sequence, and their decisions combined. We proposed an original approach for decision combination based on majority voting and Stouffer’s method coupled with a univariate statistical analysis. Cross validation with depth EEG electrodes findings in three patients including patients with normal MR on visual examination showed that the late fusion approach achieves the best performance.

Published
2015
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50. Is ventricular deformation affine?

Author

Genet, Martin, von Deuster, Constantin, Stoeck, Christian T., Kozerke, Sebastian, Institute for Biomedical Engineering [ETH Zürich] (IBT), Universität Zürich [Zürich] = University of Zurich (UZH)-Department of Information Technology and Electrical Engineering [Zürich] (D-ITET), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Imaging Sciences and Biomedical Engineering Division [London], and Guy's and St Thomas' Hospital [London]-King‘s College London

Subjects
[SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph]
Abstract

International audience; Recent advances in cardiac diffusion tensor imaging (DTI) have enabled robust imaging of the in vivo heart [1]. This allows to non-invasively assess myofiber and myosheet orientations, which has been a long-standing bottleneck of patient-specific cardiac modeling [2]. Thanks to efficient sequence design, in vivo DTI has even been performed in patients [3] and at multiple cardiac phases [4]. In this study, we analyze dual phase cardiac DTI data of normal humans to investigate the affinity of ventricular deformation. The angulation data is compared to predictions based on myofiber transportation by macroscopic deformation, to quantify the potential relative sliding between myofibers.

Published
2015

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