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13 results on '"Philips Research [Germany]"'

1. Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation

Author

Olivier Rouvière, Paul Cezar Moldovan, Anna Vlachomitrou, Sylvain Gouttard, Benjamin Riche, Alexandra Groth, Mark Rabotnikov, Alain Ruffion, Marc Colombel, Sébastien Crouzet, Juergen Weese, Muriel Rabilloud, Hôpital Edouard Herriot [CHU - HCL], Hospices Civils de Lyon (HCL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Philips Research [Germany], Philips Research, Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Centre pour l'innovation en cancérologie de Lyon (CICLY), Université de Lyon-Université de Lyon, Philips Research Europe - Hamburg, Sector Medical Imaging Systems, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, Deep Learning, [SDV]Life Sciences [q-bio], Image Processing, Computer-Assisted, Prostate, Humans, Radiology, Nuclear Medicine and imaging, General Medicine, Magnetic Resonance Imaging, ComputingMilieux_MISCELLANEOUS, Algorithms, Pelvis
Abstract

To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation.The algorithm, combining model-based and deep learning-based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm's mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression.Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm's median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists' delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation.The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging.• Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma). • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks. • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists.

Published
2022

2. QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

Author

Renaud Hedouin, Jakob Meineke, Carlos Milovic, Berkin Bilgic, Christian Langkammer, Kwok-Shing Chan, José P. Marques, Ferdinand Schweser, Wietske van der Zwaag, Donders Institute for Brain, Cognition and Behaviour, Radboud University [Nijmegen], Philips Research [Germany], Philips Research, Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Pontificia Universidad Católica de Chile (UC), Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Harvard-MIT Division of Health Sciences and Technology [Cambridge], Massachusetts Institute of Technology (MIT), 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), Spinoza Center for Neuroimaging [Amsterdam], Medical University of Graz, State University of New York [Buffalo], Radboud university [Nijmegen], Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile., Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile., Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), 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), spinoza centre for neuroimaging, Center for biomedical imaging [Bufalo], University at Buffalo [SUNY] (SUNY Buffalo), State University of New York (SUNY)-State University of New York (SUNY), center for biomedical imaging [Buffalo], 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), and Spinoza Centre for Neuroimaging

Subjects
Source code, Computer science, Property (programming), media_common.quotation_subject, computer.software_genre, Imaging phantom, 030218 nuclear medicine & medical imaging, law.invention, Full Papers—Computer Processing and Modeling, 03 medical and health sciences, 0302 clinical medicine, law, Voxel, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Cartesian coordinate system, Computer vision, Computer Simulation, media_common, Ground truth, in silico head phantom, quantitative susceptibility mapping, Full Paper, business.industry, Phantoms, Imaging, [SCCO.NEUR]Cognitive science/Neuroscience, Relaxation (iterative method), Brain, Quantitative susceptibility mapping, MRI simulations, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Magnetic Resonance Imaging, Artificial intelligence, business, computer, Head, 030217 neurology & neurosurgery, Algorithms
Abstract

Purpose To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM. Methods We created a digital whole-head tissue property phantom by segmenting and postprocessing high-resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady-state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion-weighted imaging phantom data are provided, allowing future investigations of tissue-microstructure effects in phase and QSM algorithms. Results The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom's properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial-volume effects or complex distributions of frequency shifts within the voxel. Conclusion The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.

Published
2021

3. Cardiac displacement tracking with data assimilation combining a biomechanical model and an automatic contour detection

Author

Gautier Bureau, Dominique Chapelle, Jürgen Weese, Jaroslav Tintera, Radomir Chabiniok, Alexandra Groth, Philippe Moireau, 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), King‘s College London, Philips Research [Germany], Philips Research, Institute for Clinical and Experimental Medicine (IKEM), Zemzemi, Nejib, Ozenne, Valéry, Vigmond, Edward, Coudière, Yves, 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
Computational model, Computer science, business.industry, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 0206 medical engineering, 02 engineering and technology, Tracking (particle physics), 020601 biomedical engineering, Biophysical heart modeling, cine MRI, Displacement (vector), Bottleneck, 030218 nuclear medicine & medical imaging, Term (time), Cine mri, 03 medical and health sciences, 0302 clinical medicine, Data assimilation, Computer vision, Artificial intelligence, business, [SDV.IB.BIO]Life Sciences [q-bio]/Bioengineering/Biomaterials, Cardiac imaging
Abstract

International audience; Data assimilation in computational models represents an essential step in building patient-specific simulations. This work aims at circumventing one major bottleneck in the practical use of data assimilation strategies in cardiac applications, namely, the difficulty of formulating and effectively computing adequate data-fitting term for cardiac imaging such as cine MRI. We here provide a proof-of-concept study of data assimilation based on automatic contour detection. The tissue motion simulated by the data assimilation framework is then assessed with displacements extracted from tagged MRI in six subjects, and the results illustrate the performance of the proposed method, including for circumferential displacements, which are not well extracted from cine MRI alone.

Published
2019

4. Spectral Photon-counting CT: Initial Experience with Dual-Contrast Agent K-Edge Colonography

Author

Heiner Daerr, Ernst J. Rummeny, Bernhard Brendel, Loic Boussel, Philippe Douek, Peter B. Noël, Daniela Muenzel, Ira Blevis, Ewald Roessl, Julia Herzen, Franz Pfeiffer, Daniel Bar-Ness, Philippe Coulon, Felix K. Kopp, Roland Proksa, Stefan Ruschke, Matthias Bartels, Alexander A. Fingerle, Axel Thran, Michal Rokni, Philips Healthcare, Philips Research [Germany], Philips Research, Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), 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), Institut National des Sciences Appliquées (INSA)-Université de Lyon-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), Institut National des Sciences Appliquées (INSA)-Université de Lyon-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), Physics Department, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Department of Radiology, Cardiological Hospital, Hospices Civils de Lyon, 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), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Gadolinium, media_common.quotation_subject, Iodine Compounds, chemistry.chemical_element, Lumen (anatomy), Contrast Media, Iodine, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Medicine, Contrast (vision), Radiology, Nuclear Medicine and imaging, media_common, Photons, business.industry, Phantoms, Imaging, Photon counting, digestive system diseases, chemistry, K-edge, 030220 oncology & carcinogenesis, business, Nuclear medicine, Material decomposition, Colonography, Computed Tomographic
Abstract

[DOI:\hrefhttps://dx.doi.org/10.1148/radiol.201616089010.1148/radiol.2016160890] [PubMed:\hrefhttps://www.ncbi.nlm.nih.gov/pubmed/2791870927918709]; International audience; Purpose To investigate the feasibility of using spectral photon-counting computed tomography (CT) to differentiate between gadolinium-based and nonionic iodine-based contrast material in a colon phantom by using the characteristic k edge of gadolinium. Materials and Methods A custom-made colon phantom was filled with nonionic iodine-based contrast material, and a gadolinium-filled capsule representing a contrast material-enhanced polyp was positioned on the colon wall. The colon phantom was scanned with a preclinical spectral photon-counting CT system to obtain spectral and conventional data. By fully using the multibin spectral information, material decomposition was performed to generate iodine and gadolinium maps. Quantitative measurements were performed within the lumen and polyp to quantitatively determine the absolute content of iodine and gadolinium. Results In a conventional CT section, absorption values of both contrast agents were similar at approximately 110 HU. Contrast material maps clearly differentiated the distributions, with gadolinium solely in the polyp and iodine in the lumen of the colon. Quantitative measurements of contrast material concentrations in the colon and polyp matched well with those of actual prepared mixtures. Conclusion Dual-contrast spectral photon-counting CT colonography with iodine-filled lumen and gadolinium-tagged polyps may enable ready differentiation between polyps and tagged fecal material. © RSNA, 2016.

Published
2016

5. Photon counting spectral CT component analysis of coronary artery atherosclerotic plaque samples

Author

Gerhard Martens, Axel Thran, Monica Sigovan, Loic Boussel, Ewald Roessl, Philippe Douek, Philippe Coulon, Service de Radiologie [Hôpital de la Croix-Rousse - HCL], Hôpital de la Croix-Rousse [CHU - HCL], Hospices Civils de Lyon ( HCL ) -Hospices Civils de Lyon ( HCL ), 1 - Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales ( MOTIVATE ), 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 ), Philips Healthcare, Philips Research [Germany], Philips Research, Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), 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), and 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)

Subjects
Photon, Materials science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Contrast Media, Coronary Artery Disease, Coronary artery disease, medicine, Humans, Scattering, Radiation, Radiology, Nuclear Medicine and imaging, [ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging, Photons, Full Paper, business.industry, Compton scattering, General Medicine, medicine.disease, Photon counting, Plaque, Atherosclerotic, Intensity (physics), Coronary arteries, medicine.anatomical_structure, Radiographic Image Interpretation, Computer-Assisted, Tomography, Autopsy, Nuclear medicine, business, Tomography, X-Ray Computed, Artery
Abstract

International audience; Objectives. To evaluate the capabilities of Photon Counting Spectral CT to differentiate components of coronary atherosclerotic plaque based on differences in spectral attenuation and iodine-based contrast agent concentration. Methods. 10 calcified and 13 lipid-rich atheromatous plaques from postmortem human coronary arteries were scanned with a Photon Counting Spectral CT scanner. Individual photons were counted and classified in one of 6 energy bins from 25 to 70 keV.Based on a maximum likelihood approach, maps of Photoelectric Absorption (PA), Compton Scattering (CS) and Iodine Concentration (IC) were reconstructed. Intensity measurements were performed on each map in the vessel wall, the surrounding perivascular fat, the lipid-rich and the calcified plaques. PA and CS values are expressed relative to pure water values. Comparison between these different elements was performed using Kruskal-Wallis tests with pairwise post-hoc Mann-Whitney U tests and Sidak p-values adjustment. Results. Results for vessel wall, surrounding perivascular fat, lipid-rich, and calcified plaques were respectively 1.19+/-0.09, 0.73+/-0.05, 1.08+/-0.14, 17.79+/-6.70 for PA; 0.96+/-0.02, 0.83+/-0.02, 0.91+/-0.03, 2.53+/-0.63 for CS, and 83.3+/-10.1, 37.6+/-8.1, 55.2+/-14.0, 4.9+/-20.0 mmol/l for IC, with significant differences between all tissues for PA, CS and IC (p

Published
2014

6. Deconvolution for limited-view streak artifacts removal: improvements upon an existing approach

Author

Michael Grass, Françoise Peyrin, Vincent Auvray, Loic Boussel, Bo Zhang, Cyril Mory, Philippe Douek, Simon Rit, Dirk Schäfer, Imagerie Tomographique et Radiothérapie, 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), MedisysResearch Lab (Medisys), Philips Research, Philips Research [Germany], Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Service de Radiologie [Hôpital de la Croix-Rousse - HCL], Hôpital de la Croix-Rousse [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), 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), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Artifact (error), Radon transform, business.industry, Iterative method, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Streak, Inverse, Iterative reconstruction, Inverse problem, 01 natural sciences, 030218 nuclear medicine & medical imaging, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, Computer vision, Artificial intelligence, Deconvolution, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Mathematics
Abstract

International audience; Cardiac C-Arm computed tomography leads to a view-starved reconstruction problem because of electrocardiogram gating. Reconstruction with the Feldkamp, Davis and Kress method (FDK) generates large streak artifacts in the reconstructed volumes, hampering the medical interpretation. In order to remove these artifacts, deconvolution techniques have been proposed. In this paper, we start from a recent inverse filtering method developed for streak artifact removal. Two ways to improve upon this method are described. It is then proposed to replace inverse filtering with an iterative deconvolution scheme. Finally, we show that the iterative deconvolution method can itself be replaced by iterative filtered back projection (IFBP). The IFBP approach is flexible and could be used in a broad range of applications, while the improved inverse filtering approaches are computationally less demanding and better suited for time-critical applications.

Published
2012

7. Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

Author

Julia A. Schnabel, Olivier Commowick, Grégoire Malandain, Kaifang Du, Mattias P. Heinrich, Jamie R. McClelland, M. de Bruijne, Nicholas Ayache, Max A. Viergever, Manuel Werlberger, Nicholas J. Tustison, Sascha E. A. Muenzing, Nassir Navab, Jan Ehrhardt, Dante De Nigris, Kunlin Cao, Marc Modat, Xiang Deng, Martin Urschler, D. L. Collins, James C. Gee, Cristian Lorenz, Gregory C. Sharp, Berend C. Stoel, Joseph M. Reinhardt, Rui Li, Xiao Han, Keelin Murphy, Jef Vandemeulebroucke, Simon Rit, M. Peroni, Tom Vercauteren, Sven Kabus, Stefan Klein, Marius Staring, Nikos Paragios, Gang Song, Dirk Loeckx, Dirk Smeets, David Sarrut, Sebastien Ourselin, Josien P. W. Pluim, B. van Ginneken, Jon Sporring, V. Garcia, Alexander Schmidt-Richberg, Kai Ding, Vladlena Gorbunova, Ben Glocker, Brian B. Avants, René Werner, Tal Arbel, Gary E. Christensen, Mark Jenkinson, Image sciences institute - University of Utrecht (ISI), University Medical Center [Utrecht], Department of Electrical and Computer Engineering [Iowa], University of Iowa [Iowa City], Philips Research [Germany], Philips Research, Siemens [Beijing], China Corporate Technology, State Key Laboratory of Chemical Engineering (Polymerization Division), Zhejiang University, Service DREAM, 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), Mauna Kea Technologies, Analysis and Simulation of Biomedical Images (ASCLEPIOS), 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), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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), Computer Aided Medical Procedures & Augmented Reality (CAMPAR), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN), Ecole Centrale Paris-Inria Saclay - Ile de France, Mathématiques Appliquées aux Systèmes - EA 4037 (MAS), Ecole Centrale Paris, Department of Computer Science [Copenhagen] (DIKU), Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Biomedical Imaging Group [Rotterdam] (BIG), Erasmus University Medical Center [Rotterdam] (Erasmus MC), CMS Software, Elekta Inc. [Maryland Heights], Institute of Biomedical Engineering [Oxford] (IBME), University of Oxford [Oxford], Department of Clinical Neurology [Oxford], University of Oxford [Oxford]-FMRIB Centre- John Radcliffe Hospital [Oxford University Hospital], Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), Centre for Intelligent Machines (CIM), McGill University = Université McGill [Montréal, Canada], Montreal Neurological Institute and Hospital, McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Massachusetts General Hospital [Boston], Institute of Medical Informatics [Lübeck], Universität zu Lübeck [Lübeck], Medical imaging research center [Leuven], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven)-Faculty of Engineering, Penn Image Computing & Science Lab [Philadelphia] (PICSL), University of Pennsylvania [Philadelphia], Department of radiology and medical imaging [Charlottesville], University of Virginia [Charlottesville], Division of image processing [Leiden], Leiden University Medical Center (LUMC)-Department of Radiology, Institute for Computer Graphics and Vision [Graz] (ICG), Graz University of Technology [Graz] (TU Graz), 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), Imagerie Tomographique et Radiothérapie, 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), Commowick, Olivier, 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-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)-Ecole Centrale Paris, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), University of Oxford, University of Oxford-FMRIB Centre- John Radcliffe Hospital [Oxford University Hospital], Universität zu Lübeck = University of Lübeck [Lübeck], University of Pennsylvania, University of Virginia, 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 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), Electronics and Informatics, Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden-Department of Radiology, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-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 de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-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), Medical Image Analysis, Rehabilitation Medicine, Radiology & Nuclear Medicine, and Neurology

Subjects
Databases, Factual, Computer science, Software Validation, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Computed tomography, Aetiology, screening and detection [ONCOL 5], MESH: Observer Variation, 030218 nuclear medicine & medical imaging, Task (project management), 0302 clinical medicine, MESH: Animals, Segmentation, Observer Variation, evaluation, Radiological and Ultrasound Technology, medicine.diagnostic_test, Reference Standards, Thorax, Computer Science Applications, Test (assessment), Radiographic Image Enhancement, MESH: Reproducibility of Results, Chest, Radiographic Image Interpretation, Computer-Assisted, Radiography, Thoracic, MESH: Reference Standards, [SDV.IB]Life Sciences [q-bio]/Bioengineering, MESH: Tomography, X-Ray Computed, Algorithms, medicine.medical_specialty, MESH: Sheep, Image registration, MESH: Algorithms, Sensitivity and Specificity, MESH: Thorax, lung, Set (abstract data type), 03 medical and health sciences, registration, MESH: Radiographic Image Interpretation, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Medical imaging, medicine, Animals, MESH: Lung, Medical physics, Electrical and Electronic Engineering, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Sheep, MESH: Software Validation, Information retrieval, Poverty-related infectious diseases [N4i 3], computed tomography, Image segmentation, MESH: Radiography, Thoracic, MESH: Databases, Factual, MESH: Sensitivity and Specificity, MESH: Radiographic Image Enhancement, Algorithm design, reproducibility of results, Tomography, X-Ray Computed, 030217 neurology & neurosurgery, Software
Abstract

Contains fulltext : 96888.pdf (Publisher’s version ) (Open Access) EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

Published
2011

8. Theory of Optimal Mixing in Di rectly Modulated Laser Diodes

Author

S., Khorasani, B., Cabon, Philips Research [Germany], Philips Research, Institut de Microélectronique, Electromagnétisme et Photonique - Laboratoire d'Hyperfréquences et Caractérisation (IMEP-LAHC), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2009

9. Robust pose estimation and recognition using non-Gaussian modeling of appearance subspaces

Author

Fabrice Heitz, Pierre Charbonnier, T. Vik, Philips Research [Germany], Philips Research, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), Centre d'études techniques de l'équipement Est (CETE Est), Avant création Cerema, and Cadic, Ifsttar

Subjects
Gaussian, Normal Distribution, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, MODELE STATISTIQUE, Information Storage and Retrieval, 02 engineering and technology, Sensitivity and Specificity, Pattern Recognition, Automated, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Imaging, Three-Dimensional, Artificial Intelligence, ALGORITHME, Image Interpretation, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Mean-shift, TRAITEMENT DES IMAGES, Gaussian process, Pose, Mathematics, Models, Statistical, RECONNAISSANCE DE FORME, business.industry, Applied Mathematics, Cognitive neuroscience of visual object recognition, Reproducibility of Results, Pattern recognition, Visual appearance, Image Enhancement, Active appearance model, ComputingMethodologies_PATTERNRECOGNITION, Computational Theory and Mathematics, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Pattern recognition (psychology), symbols, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, 030217 neurology & neurosurgery, Software, Algorithms
Abstract

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm.

Published
2007

10. Acceleration of tissue phase mapping by k-t BLAST: a detailed analysis of the influence of k-t-BLAST for the quantification of myocardial motion at 3T

Author

Patrick Etyngier, Robert Manzke, Volker Rasche, Axel Bornstedt, Anja Lutz, G. Ulrich Nienhaus, Department of Internal Medicine II, Universitätsklinikum Ulm - University Hospital of Ulm, Philips Research [Germany], Philips Research, MedisysResearch Lab (Medisys), and Karlsruhe Institute of Technology (KIT)

Subjects
Adult, Male, lcsh:Diseases of the circulatory (Cardiovascular) system, Time Factors, Adolescent, Magnetic Resonance Imaging, Cine, Motion (physics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Acceleration, Young Adult, 0302 clinical medicine, Data acquisition, Cardiac motion, Germany, Image Interpretation, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Phase mapping, Medicine(all), Radiological and Ultrasound Technology, business.industry, Research, Heart, Myocardial Contraction, lcsh:RC666-701, Temporal resolution, Dynamic contrast-enhanced MRI, Myocardial motion, Female, Cardiology and Cardiovascular Medicine, Nuclear medicine, business, 030217 neurology & neurosurgery, Biomedical engineering
Abstract

International audience; Background: The assessment of myocardial motion with tissue phase mapping (TPM) provides high spatiotemporal resolution and quantitative motion information in three directions. Today, whole volume coverage of the heart by TPM encoding at high spatial and temporal resolution is limited by long data acquisition times. Therefore, a significant increase in imaging speed without deterioration of the quantitative motion information is required. For this purpose, the k-t BLAST acceleration technique was combined with TPM black-blood functional imaging of the heart. Different k-t factors were evaluated with respect to their impact on the quantitative assessment of cardiac motion. Results: It is demonstrated that a k-t BLAST factor of two can be used with a marginal, but statistically significant deterioration of the quantitative motion data. Further increasing the k-t acceleration causes substantial alteration of the peak velocities and the motion pattern, but the temporal behavior of the contraction is well maintained up to an acceleration factor of six. Conclusions: The application of k-t BLAST for the acceleration of TPM appears feasible. A reduction of the acquisition time of almost 45% could be achieved without substantial loss of quantitative motion information. © 2011 Lutz et al; licensee BioMed Central Ltd. The electronic version of this article is the complete one and can be found online at: http://www.jcmr-online.com/content/13/1/5.

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11. Asymptomatic Cocaine Abuse　- Myocardial Tissue Characterization Using Cardiac Biomarkers and Cardiovascular Magnetic Resonance Imaging.

Author

Radunski UK, Fuger U, Bohnen S, Lund GK, Stehning C, Zeller T, Tahir E, Avanesov M, Adam G, Blankenberg S, Reimer J, and Muellerleile K

Subjects
Adult, Biomarkers blood, Cardiomyopathies diagnostic imaging, Cardiomyopathies pathology, Case-Control Studies, Cocaine-Related Disorders pathology, Edema diagnostic imaging, Edema etiology, Female, Fibrosis diagnostic imaging, Fibrosis etiology, Focal Adhesions, Humans, Magnetic Resonance Imaging methods, Male, Troponin I blood, Cardiomyopathies diagnosis, Cocaine-Related Disorders complications
Abstract

Background: Use of cocaine is widespread and associated with several cardiovascular diseases. Recent CMR studies indicate frequent myocardial scar/fibrosis in asymptomatic cocaine abusers (CA).Methods and Results:This study used a combination of advanced CMR tissue characterization techniques, including late gadolinium enhancement (LGE) for focal, and extracellular volume (ECV) imaging for diffuse myocardial injury/fibrosis, with circulating biomarkers for a comprehensive characterization of myocardial injury. We included 20 cardiac asymptomatic CA and a control group of 20 healthy volunteers. The comprehensive assessment included physical examination, resting ECG, exercise ECG, cardiac biomarkers, transthoracic echocardiogram and CMR. We did not find significant differences between CA and controls either in functional CMR parameters such as LVEDVi, LVESVi, LVEF, LV mass index, or in global myocardial ECV. Neither CA nor controls had evidence of myocardial edema on T2-weighted CMR, but 8 CA (40%), and none of the controls had focal myocardial scar (P<0.01). Interestingly, CA with focal myocardial scar on LGE had significantly higher high-sensitivity troponin I (hs-TNI) compared with CA without focal scar (median, 1.7 ng/L; IQR, 1.3-2.5 ng/L vs. 0.6 ng/L; 0.4-1.3 ng/L; P<0.01)., Conclusions: Focal myocardial injury in terms of subtle LGE in 40% of asymptomatic CA was associated with higher hs-TNI. Comprehensive assessment including advanced ECV imaging indicates a focal rather than diffuse pattern of myocardial involvement in asymptomatic CA.

Published
2017
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12. T1 and T2 mapping cardiovascular magnetic resonance imaging techniques reveal unapparent myocardial injury in patients with myocarditis.

Author

Radunski UK, Lund GK, Säring D, Bohnen S, Stehning C, Schnackenburg B, Avanesov M, Tahir E, Adam G, Blankenberg S, and Muellerleile K

Subjects
Adult, Case-Control Studies, Contrast Media, Female, Humans, Male, Meglumine analogs & derivatives, Middle Aged, Myocarditis pathology, Organometallic Compounds, Predictive Value of Tests, Severity of Illness Index, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Cine methods, Myocarditis diagnostic imaging, Myocardium pathology
Abstract

Introduction: This study evaluated the ability of T1 and T2 mapping cardiovascular magnetic resonance (CMR) to detect myocardial injury in apparently normal myocardium of patients with myocarditis., Materials and Methods: We included 20 patients with "infarct-like" acute myocarditis who had typical focal myocardial lesions on late gadolinium enhancement (LGE) images as well as 20 healthy controls. The CMR protocol consisted of a standard myocarditis protocol which was combined with T1 (modified Look-Locker inversion recovery (MOLLI) with a 3(3)5 scheme and T2 mapping (hybrid gradient- and spin-echo multi-echo sequence, GraSE). First, LGE images were used to depict focal myocardial injury and apparently normal, remote myocardium. Second, native T1, T2 and ECV values were obtained in focal lesions but also in apparently normal myocardium. Third, native T1, T2 and ECV values ≥2 standard deviations above reference values obtained in healthy volunteers were used to quantify myocardial injury in patients with myocarditis., Results: Apparently normal myocardium had significantly higher median native T1 [1095 (1055-1148) ms] and ECV [34 (32-35) %] values compared to reference values from healthy volunteers, which were 1051 (1021-1064) ms (p < 0.01) and 26 (24-27) % (p < 0.0001). Furthermore, a nonsignificant increase in median myocardial T2 was detected in apparently normal myocardium of patients with myocarditis compared to healthy volunteers [59 (55-65) vs. 56 (54-60) ms; p = 0.18]. Consequently, the amount of myocardial injury was significantly larger on native T1 [48 (32-56) %; p < 0.01] and ECV maps [58 (50-66) %; p < 0.01] compared to LGE [14 (9-20) %]., Conclusions: Native T1 and ECV maps reveal hidden myocardial injury in normal appearing myocardium of patients with myocarditis. The amount of myocardial injury in myocarditis was underestimated by conventional LGE imaging.

Published
2017
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