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57 results on '"Benjamin De Leener"'

1. Severe central nervous system demyelination in Sanfilippo disease

Author

Mahsa Taherzadeh, Erjun Zhang, Irene Londono, Benjamin De Leener, Sophie Wang, Jonathan D. Cooper, Timothy E. Kennedy, Carlos R. Morales, Zesheng Chen, Gregory A. Lodygensky, and Alexey V. Pshezhetsky

Subjects
mucopolysaccharidosis, oligodendrocyte, myelination, lysosomal storage, GM3 ganglioside, diffusion basis spectrum imaging, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

IntroductionChronic progressive neuroinflammation is a hallmark of neurological lysosomal storage diseases, including mucopolysaccharidosis III (MPS III or Sanfilippo disease). Since neuroinflammation is linked to white matter tract pathology, we analyzed axonal myelination and white matter density in the mouse model of MPS IIIC HgsnatP304L and post-mortem brain samples of MPS III patients.MethodsBrain and spinal cord tissues of human MPS III patients, 6-month-old HgsnatP304L mice and age- and sex-matching wild type mice were analyzed by immunofluorescence to assess levels of myelin-associated proteins, primary and secondary storage materials, and levels of microgliosis. Corpus callosum (CC) region was studied by transmission electron microscopy to analyze axon myelination and morphology of oligodendrocytes and microglia. Mouse brains were analyzed ex vivo by high-filed MRI using Diffusion Basis Spectrum Imaging in Python-Diffusion tensor imaging algorithms.ResultsAnalyses of CC and spinal cord tissues by immunohistochemistry revealed substantially reduced levels of myelin-associated proteins including Myelin Basic Protein, Myelin Associated Glycoprotein, and Myelin Oligodendrocyte Glycoprotein. Furthermore, ultrastructural analyses revealed disruption of myelin sheath organization and reduced myelin thickness in the brains of MPS IIIC mice and human MPS IIIC patients compared to healthy controls. Oligodendrocytes (OLs) in the CC of MPS IIIC mice were scarce, while examination of the remaining cells revealed numerous enlarged lysosomes containing heparan sulfate, GM3 ganglioside or “zebra bodies” consistent with accumulation of lipids and myelin fragments. In addition, OLs contained swollen mitochondria with largely dissolved cristae, resembling those previously identified in the dysfunctional neurons of MPS IIIC mice. Ex vivo Diffusion Basis Spectrum Imaging revealed compelling signs of demyelination (26% increase in radial diffusivity) and tissue loss (76% increase in hindered diffusivity) in CC of MPS IIIC mice.DiscussionOur findings demonstrate an important role for white matter injury in the pathophysiology of MPS III. This study also defines specific parameters and brain regions for MRI analysis and suggests that it may become a crucial non-invasive method to evaluate disease progression and therapeutic response.

Published
2023
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2. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Author

Julien Cohen-Adad, Eva Alonso-Ortiz, Mihael Abramovic, Carina Arneitz, Nicole Atcheson, Laura Barlow, Robert L. Barry, Markus Barth, Marco Battiston, Christian Büchel, Matthew Budde, Virginie Callot, Anna J. E. Combes, Benjamin De Leener, Maxime Descoteaux, Paulo Loureiro de Sousa, Marek Dostál, Julien Doyon, Adam Dvorak, Falk Eippert, Karla R. Epperson, Kevin S. Epperson, Patrick Freund, Jürgen Finsterbusch, Alexandru Foias, Michela Fratini, Issei Fukunaga, Claudia A. M. Gandini Wheeler-Kingshott, Giancarlo Germani, Guillaume Gilbert, Federico Giove, Charley Gros, Francesco Grussu, Akifumi Hagiwara, Pierre-Gilles Henry, Tomáš Horák, Masaaki Hori, James Joers, Kouhei Kamiya, Haleh Karbasforoushan, Miloš Keřkovský, Ali Khatibi, Joo-Won Kim, Nawal Kinany, Hagen H. Kitzler, Shannon Kolind, Yazhuo Kong, Petr Kudlička, Paul Kuntke, Nyoman D. Kurniawan, Slawomir Kusmia, René Labounek, Maria Marcella Laganà, Cornelia Laule, Christine S. Law, Christophe Lenglet, Tobias Leutritz, Yaou Liu, Sara Llufriu, Sean Mackey, Eloy Martinez-Heras, Loan Mattera, Igor Nestrasil, Kristin P. O’Grady, Nico Papinutto, Daniel Papp, Deborah Pareto, Todd B. Parrish, Anna Pichiecchio, Ferran Prados, Àlex Rovira, Marc J. Ruitenberg, Rebecca S. Samson, Giovanni Savini, Maryam Seif, Alan C. Seifert, Alex K. Smith, Seth A. Smith, Zachary A. Smith, Elisabeth Solana, Y. Suzuki, George Tackley, Alexandra Tinnermann, Jan Valošek, Dimitri Van De Ville, Marios C. Yiannakas, Kenneth A. Weber II, Nikolaus Weiskopf, Richard G. Wise, Patrik O. Wyss, and Junqian Xu

Subjects
Science
Abstract

Measurement(s) spinal cord Technology Type(s) magnetic resonance imaging Factor Type(s) manufacturer • site Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Canada • Switzerland • Australia • United States of America • United Kingdom • Germany • French Republic • Czech Republic • Italy • Japan • Kingdom of Spain • China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14052269

Published
2021
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3. Investigating the human spinal sensorimotor pathways through functional magnetic resonance imaging

Author

Caroline Landelle, Ovidiu Lungu, Shahabeddin Vahdat, Anne Kavounoudias, Véronique Marchand-Pauvert, Benjamin De Leener, and Julien Doyon

Subjects
Spinal-cord FMRI, Sensorimotor pathways, Motor, Proprioception, Touch, resting-state, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Most of our knowledge about the human spinal ascending (sensory) and descending (motor) pathways comes from non-invasive electrophysiological investigations. However, recent methodological advances in acquisition and analyses of functional magnetic resonance imaging (fMRI) data from the spinal cord, either alone or in combination with the brain, have allowed us to gain further insights into the organization of this structure. In the current review, we conducted a systematic search to produced somatotopic maps of the spinal fMRI activity observed through different somatosensory, motor and resting-state paradigms. By cross-referencing these human neuroimaging findings with knowledge acquired through neurophysiological recordings, our review demonstrates that spinal fMRI is a powerful tool for exploring, in vivo, the human spinal cord pathways. We report strong cross-validation between task-related and resting-state fMRI in accordance with well-known hemicord, postero-anterior and rostro-caudal organization of these pathways. We also highlight the specific advantages of using spinal fMRI in clinical settings to characterize better spinal-related impairments, predict disease progression, and guide the implementation of therapeutic interventions.

Published
2021
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4. Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Author

Julien Cohen-Adad, Eva Alonso-Ortiz, Mihael Abramovic, Carina Arneitz, Nicole Atcheson, Laura Barlow, Robert L. Barry, Markus Barth, Marco Battiston, Christian Büchel, Matthew Budde, Virginie Callot, Anna J. E. Combes, Benjamin De Leener, Maxime Descoteaux, Paulo Loureiro de Sousa, Marek Dostál, Julien Doyon, Adam Dvorak, Falk Eippert, Karla R. Epperson, Kevin S. Epperson, Patrick Freund, Jürgen Finsterbusch, Alexandru Foias, Michela Fratini, Issei Fukunaga, Claudia A. M. Gandini Wheeler-Kingshott, Giancarlo Germani, Guillaume Gilbert, Federico Giove, Charley Gros, Francesco Grussu, Akifumi Hagiwara, Pierre-Gilles Henry, Tomáš Horák, Masaaki Hori, James Joers, Kouhei Kamiya, Haleh Karbasforoushan, Miloš Keřkovský, Ali Khatibi, Joo-Won Kim, Nawal Kinany, Hagen H. Kitzler, Shannon Kolind, Yazhuo Kong, Petr Kudlička, Paul Kuntke, Nyoman D. Kurniawan, Slawomir Kusmia, René Labounek, Maria Marcella Laganà, Cornelia Laule, Christine S. Law, Christophe Lenglet, Tobias Leutritz, Yaou Liu, Sara Llufriu, Sean Mackey, Eloy Martinez-Heras, Loan Mattera, Igor Nestrasil, Kristin P. O’Grady, Nico Papinutto, Daniel Papp, Deborah Pareto, Todd B. Parrish, Anna Pichiecchio, Ferran Prados, Àlex Rovira, Marc J. Ruitenberg, Rebecca S. Samson, Giovanni Savini, Maryam Seif, Alan C. Seifert, Alex K. Smith, Seth A. Smith, Zachary A. Smith, Elisabeth Solana, Y. Suzuki, George Tackley, Alexandra Tinnermann, Jan Valošek, Dimitri Van De Ville, Marios C. Yiannakas, Kenneth A. Weber II, Nikolaus Weiskopf, Richard G. Wise, Patrik O. Wyss, and Junqian Xu

Subjects
Science
Published
2021
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5. Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis

Author

Marios C. Yiannakas, Ahmed M. Mustafa, Benjamin De Leener, Hugh Kearney, Carmen Tur, Daniel R. Altmann, Floriana De Angelis, Domenico Plantone, Olga Ciccarelli, David H. Miller, Julien Cohen-Adad, and Claudia A.M. Gandini Wheeler-Kingshott

Subjects
Magnetic resonance imaging, Image segmentation, Cord cross-sectional area, Grey matter, White matter, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Spinal cord (SC) atrophy, i.e. a reduction in the SC cross-sectional area (CSA) over time, can be measured by means of image segmentation using magnetic resonance imaging (MRI). However, segmentation methods have been limited by factors relating to reproducibility or sensitivity to change. The purpose of this study was to evaluate a fully automated SC segmentation method (PropSeg), and compare this to a semi-automated active surface (AS) method, in healthy controls (HC) and people with multiple sclerosis (MS). MRI data from 120 people were retrospectively analysed; 26 HC, 21 with clinically isolated syndrome, 26 relapsing remitting MS, 26 primary and 21 secondary progressive MS. MRI data from 40 people returning after one year were also analysed. CSA measurements were obtained within the cervical SC. Reproducibility of the measurements was assessed using the intraclass correlation coefficient (ICC). A comparison between mean CSA changes obtained with the two methods over time was performed using multivariate structural equation regression models. Associations between CSA measures and clinical scores were investigated using linear regression models. Compared to the AS method, the reproducibility of CSA measurements obtained with PropSeg was high, both in patients and in HC, with ICC > 0.98 in all cases. There was no significant difference between PropSeg and AS in terms of detecting change over time. Furthermore, PropSeg provided measures that correlated with physical disability, similar to the AS method. PropSeg is a time-efficient and reliable segmentation method, which requires no manual intervention, and may facilitate large multi-centre neuroprotective trials in progressive MS.

Published
2016
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7. Monitoring for myelopathic progression with multiparametric quantitative MRI.

Author

Allan R Martin, Benjamin De Leener, Julien Cohen-Adad, Sukhvinder Kalsi-Ryan, David W Cadotte, Jefferson R Wilson, Lindsay Tetreault, Aria Nouri, Adrian Crawley, David J Mikulis, Howard Ginsberg, Eric M Massicotte, and Michael G Fehlings

Subjects
Medicine, Science
Abstract

Patients with mild degenerative cervical myelopathy (DCM) are often managed non-operatively, and surgery is recommended if neurological progression occurs. However, detection of progression is often subjective. Quantitative MRI (qMRI) directly measures spinal cord (SC) tissue changes, detecting axonal injury, demyelination, and atrophy. This longitudinal study compared multiparametric qMRI with clinical measures of progression in non-operative DCM patients.26 DCM patients were followed. Clinical data included modified Japanese Orthopedic Association (mJOA) and additional assessments. 3T qMRI data included cross sectional area, diffusion fractional anisotropy, magnetization transfer ratio, and T2*-weighted white/grey matter signal ratio, extracted from the compressed SC and above/below. Progression was defined as 1) patients' subjective impression, 2) 2-point mJOA decrease, 3) ≥3 clinical measures worsening ≥5%, 4) increased compression on MRI, or 5) ≥1 of 10 qMRI measures or composite score worsening (p < 0.004, corrected).Follow-up (13.5 ± 4.9 months) included mJOA in all 26 patients, MRI in 25, and clinical/qMRI in 22. 42.3% reported subjective worsening, compared with mJOA (11.5%), MRI (20%), comprehensive assessments (54.6%), and qMRI (68.2%). Relative to subjective worsening, qMRI showed 100% sensitivity and 53.3% specificity compared with comprehensive assessments (75%, 60%), mJOA (27.3%, 100%), and MRI (18.2%, 81.3%). A decision-making algorithm incorporating qMRI identified progression and recommended surgery for 11 subjects (42.3%).Quantitative MRI shows high sensitivity to detect myelopathic progression. Our results suggest that neuroplasticity and behavioural adaptation may mask progressive SC tissue injury. qMRI appears to be a useful method to confirm subtle myelopathic progression in individual patients, representing an advance toward clinical translation of qMRI.

Published
2018
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18. Spinal cord grey matter segmentation challenge.

Author

Ferran Prados, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio, Manuel Jorge Cardoso, Benjamin N. Conrad, Esha Datta, Gergely Dávid, Benjamin De Leener, Sara M. Dupont, Patrick Freund, Claudia A. M. Gandini Wheeler-Kingshott, Francesco Grussu, Roland G. Henry, Bennett A. Landman, Emil Ljungberg, Bailey Lyttle, Sébastien Ourselin, Nico Papinutto, Salvatore Saporito, Regina Schlaeger, Seth A. Smith, Paul E. Summers, Roger C. Tam, Marios C. Yiannakas, Alyssa H. Zhu, and Julien Cohen-Adad

Published
2017
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21. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

Author

Charley Gros, Benjamin De Leener, Atef Badji, Josefina Maranzano, Dominique Eden, Sara M. Dupont, Jason Talbott, Ren Zhuoquiong, Yaou Liu, Tobias Granberg, Russell Ouellette, Yasuhiko Tachibana, Masaaki Hori, Kouhei Kamiya, Lydia Chougar, Leszek Stawiarz, Jan Hillert, Elise Bannier, Anne Kerbrat, Gilles Edan, Pierre Labauge, Virginie Callot, Jean Pelletier, Bertrand Audoin, Henitsoa Rasoanandrianina, Jean-Christophe Brisset, Paola Valsasina, Maria Assunta Rocca, Massimo Filippi, Rohit Bakshi, Shahamat Tauhid, Ferran Prados, Marios C. Yiannakas, Hugh Kearney, Olga Ciccarelli, Seth A. Smith, Constantina Andrada Treaba, Caterina Mainero, Jennifer Lefeuvre, Daniel S. Reich, Govind Nair, Vincent Auclair, Donald G. McLaren, Allan R. Martin, Michael G. Fehlings, Shahabeddin Vahdat, Ali Khatibi, Julien Doyon, Timothy M. Shepherd, Erik Charlson, Sridar Narayanan, and Julien Cohen-Adad

Published
2018

25. Centering inclusivity in the design of online conferences - An OHBM - Open Science perspective

Author

Elizabeth Levitis, Cassandra Doris Gould van Praag, Remi Gau, Stephan Heunis, Elizabeth DuPre, Greg Kiar, Katherine L Bottenhorn, Tristan Glatard, Aki Nikolaidis, Kirstie Jane Whitaker, Matteo Mancini, Guiomar Niso, Soroosh Afyouni, Eva Alonso Ortiz, Stefan Appelhoff, Aurina Arnatkeviciute, Melvin Selim ATAY, Tibor Auer, Giulia Baracchini, Johanna Margarete Marianne Bayer, Michael J. S. Beauvais, Janine Diane Bijsterbosch, Isil Poyraz Bilgin, Saskia Bollmann, Steffen Bollmann, Rotem Botvinik-Nezer, Molly G Bright, Vince D Calhoun, Xiao Chen, Sidhant Chopra, Hu Chuan-Peng, Thomas Close, Savannah Cookson, Cameron Craddock, Alejandro De La Vega, Benjamin De Leener, Damion Demeter, Paola Di Maio, Erin W Dickie, Simon B Eickhoff, Oscar Esteban, Karolina Finc, Matteo Frigo, Saampras Ganesan, Melanie Ganz, Kelly Garner, Eduardo A. Garza-Villarreal, Gabriel Gonzalez-Escamilla, Rohit Goswami, John David Griffiths, Tijl Grootswagers, Samuel Guay, Olivia Guest, Daniel A. Handwerker, Peer Herholz, Katja Heuer, Dorien Huijser, Vittorio Iacovella, Michael Joseph, Agah Karakuzu, David Keator, Xenia Kobeleva, Manoj Kumar, Angie Laird, Linda J. Larson-Prior, Alexandra Lautarescu, Alberto Lazari, Jon Haitz Legarreta Gorroño, Jeff jeffers, Jinglei Lv, Sina Mansour L., David Meunier, Dustin Moraczewski, Tulika Nandi, Samuel A. Nastase, Matthias Nau, Stephanie Noble, Martin Norgaard, Johnes Obungoloch, Robert Oostenveld, Edwina R Orchard, Ana Luísa Pinho, Russell Poldrack, Anqi Qiu, Pradeep Reddy Raamana, Ariel Rokem, Saige Rutherford, Malvika Sharan, Thomas Shaw, Warda T Syeda, Meghan M. Testerman, Roberto Toro, Sofie L. Valk, Sofie Van Den Bossche, Gael P. Varoquaux, Frantisek Vasa, Michele Veldsman, Jakub Vohryzek, Adina Svenja Wagner, Reubs J Walsh, Tonya White, null zuxfoucault, Xihe Xie, Chao-Gan Yan, Yu-Fang Yang, Yohan Yee, Gaston E Zanitti, Ana Van Gulick, Eugene Duff, Camille MAUMET, National Institute of Mental Health (NIMH), University College of London [London] (UCL), University of Oxford, Université Catholique de Louvain = Catholic University of Louvain (UCL), Eindhoven University of Technology [Eindhoven] (TU/e), McGill University = Université McGill [Montréal, Canada], Florida International University [Miami] (FIU), Concordia University [Montreal], Child Mind Institute, The Alan Turing Institute, University of Sussex, Cardiff University, École Polytechnique de Montréal (EPM), Indiana University [Bloomington], Indiana University System, Universidad Politécnica de Madrid (UPM), Max Planck Institute for Human Development, Max-Planck-Gesellschaft, Monash University [Melbourne], Middle East Technical University [Ankara] (METU), University of Surrey (UNIS), Montreal Neurological Institute and Hospital, University of Melbourne, Orygen Youth Health Research Centre [Melbourne], Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), University of Reading (UOR), University of Queensland [Brisbane], Dartmouth College [Hanover], Northwestern University [Evanston], Georgia State University, University System of Georgia (USG), Institute of Psychology [Beijing], Chinese Academy of Sciences [Changchun Branch] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), Nanjing Normal University (NNU), The University of Sydney, University of California (UC), University of Texas at Austin [Austin], CHU Sainte Justine [Montréal], Institute for Globally Distributed Open Research and Education (IGDORE ), Centre for Addiction and Mental Health [Toronto] (CAMH), University of Toronto, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Jülich Research Centre, Université de Lausanne = University of Lausanne (UNIL), Nicolaus Copernicus University [Toruń], Université Côte d'Azur (UCA), Computational Imaging of the Central Nervous System (ATHENA), 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), Rigshospitalet [Copenhagen], Copenhagen University Hospital, University of Copenhagen = Københavns Universitet (UCPH), University of Birmingham [Birmingham], Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), University of Iceland [Reykjavik], Indian Institute of Technology Kanpur (IIT Kanpur), Western Sydney University, Université de Montréal (UdeM), Radboud University [Nijmegen], Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Erasmus University Rotterdam, Universiteit Leiden, University of Trento [Trento], Université du Québec à Montréal = University of Québec in Montréal (UQAM), University Hospital Bonn, German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Princeton University, University of Arkansas for Medical Sciences (UAMS), Arkansas Children's Research Institute, King‘s College London, Université de Sherbrooke (UdeS), Aarhus University [Aarhus], Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Yale University [New Haven], Stanford University, Mbarara University of Science and Technology [Mbarara] (MUST), Karolinska Institutet [Stockholm], Université Paris-Saclay, National University of Singapore (NUS), Johns Hopkins University (JHU), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Washington [Seattle], Radboud University Medical Center [Nijmegen], University of Michigan [Ann Arbor], University of Michigan System, Institut Pasteur [Paris] (IP), Universiteit Gent = Ghent University (UGENT), Vrije Universiteit Amsterdam [Amsterdam] (VU), Department of Public Health, Erasmus University Medical Centre, Rotterdam, Erasmus University Medical Centre, Rotterdam, Academia Sinica, Weill Medical College of Cornell University [New York], Chinese Academy of Sciences [Beijing] (CAS), University of Würzburg, The Hospital for sick children [Toronto] (SickKids), Carnegie Mellon University [Pittsburgh] (CMU), 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), R25-DA051675, National Institutes of Health, 1631325, National Science Foundation, 5R21MH118556-02, National Institute of Mental Health, BB/S008314/1, Biotechnology and Biological Sciences Research Council, G036716N, Fonds Wetenschappelijk Onderzoek, University of Oxford [Oxford], University of California, University of Lausanne (UNIL), University of Copenhagen = Københavns Universitet (KU), Universidad Nacional Autónoma de México (UNAM), Johannes Gutenberg - Universität Mainz (JGU), Radboud university [Nijmegen], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Leiden University, Institut Pasteur [Paris], Universiteit Gent = Ghent University [Belgium] (UGENT), 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), and Maumet, Camille

Subjects
SDG 16 - Peace, collaborative events, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, SDG 16 - Peace, Justice and Strong Institutions, Review, Justice and Strong Institutions, online conferences, diversity, World Wide Web, inclusivity, open science, ddc:610, Sociology
Abstract

As the global health crisis unfolded throughout the world, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical and legal barriers and effectively enabled many individuals who have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g. caregiving responsibilities. Yet, the mere existence of online conferences is unfortunately no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the setup of the conference can provide more visibility to well-established researchers and reduce opportunities for early career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design.Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to: attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

Published
2021
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26. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Author

Alexandra Tinnermann, Dimitri Van De Ville, Cornelia Laule, Sean Mackey, Robert L. Barry, Benjamin De Leener, Anna Pichiecchio, Eva Alonso-Ortiz, Giancarlo Germani, Ali Khatibi, Nico Papinutto, Marc J. Ruitenberg, René Labounek, Nawal Kinany, Francesco Grussu, Michela Fratini, Petr Kudlička, Christophe Lenglet, Kouhei Kamiya, Pierre-Gilles Henry, Julien Cohen-Adad, Richard G. Wise, Deborah Pareto, Tomáš Horák, Jürgen Finsterbusch, Alex Rovira, Todd B. Parrish, Y. Suzuki, George Tackley, Junqian Xu, Falk Eippert, Virginie Callot, Daniel Papp, Laura Barlow, Karla R. Epperson, Charley Gros, Nicole Atcheson, Akifumi Hagiwara, Jan Valošek, Yaou Liu, Patrick Freund, Maria Marcella Laganà, James M. Joers, Marco Battiston, Markus Barth, Adam V. Dvorak, Masaaki Hori, Joo Won Kim, Miloš Keřkovský, Paulo Loureiro de Sousa, Sara Llufriu, Mihael Abramovic, Igor Nestrasil, Kenneth A. Weber, Christine S. Law, Paul Kuntke, Elisabeth Solana, Kristin P. O’Grady, Alexandru Foias, Slawomir Kusmia, Claudia A. M. Wheeler-Kingshott, Hagen H. Kitzler, Alex K. Smith, Nyoman D. Kurniawan, Shannon H. Kolind, Marios C. Yiannakas, Seth A. Smith, Zachary A. Smith, Federico Giove, Tobias Leutritz, Matthew D. Budde, Rebecca S. Samson, Christian Büchel, Kevin S. Epperson, Marek Dostál, Giovanni Savini, Issei Fukunaga, Haleh Karbasforoushan, Loan Mattera, Nikolaus Weiskopf, Guillaume Gilbert, Yazhuo Kong, Ferran Prados, Maryam Seif, Anna J.E. Combes, Julien Doyon, P Wyss, Alan C. Seifert, Carina Arneitz, Eloy Martinez-Heras, Maxime Descoteaux, 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), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Universitat Oberta de Catalunya (UOC), Universitat Oberta de Catalunya. eHealth Center, Institut Català de la Salut, [Cohen-Adad J] NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada. Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada. Mila - Quebec AI Institute, Montreal, QC, Canada. [Alonso-Ortiz E] NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada. [Abramovic M, Arneitz C] Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland. [Atcheson N] Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia. [Barlow L] Department of Radiology, University of British Columbia, Vancouver, BC, Canada. [Grussu F] NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Vall d’Hebron Hospital Universitari, Barcelona, Spain. [Pareto D, Rovira À] Secció de Neuroradiologia, Vall d’Hebron Hospital Universitri, Barcelona, Spain, Vall d'Hebron Barcelona Hospital Campus, Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-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)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, Data Descriptor, medicine.medical_specialty, Sciences du Vivant [q-bio]/Neurosciences [q-bio.NC], Investigació - Avaluació, Nervous System::Central Nervous System::Spinal Cord [ANATOMY], databases, Computer science, Science, of-the-art, Spinal cord diseases, Spinal Cord Diseases, Imaging techniques, Library and Information Sciences, 030218 nuclear medicine & medical imaging, Education, Databases, 03 medical and health sciences, 0302 clinical medicine, Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], Investigative Techniques::Epidemiologic Methods::Epidemiologic Research Design::Reproducibility of Results [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], medicine, Medical physics, diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], Other subheadings::Other subheadings::/diagnostic imaging [Other subheadings], Protocol (science), Reproducibility, Extramural, Otros calificadores::Otros calificadores::/diagnóstico por imagen [Otros calificadores], técnicas de investigación::métodos epidemiológicos::diseño de la investigación epidemiológica::reproducibilidad de los resultados [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], white, Spinal cord, spinal cord diseases, Toolbox, imaging techniques, matter, Computer Science Applications, magnetization-transfer, medicine.anatomical_structure, Imatgeria per ressonància magnètica, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], sistema nervioso::sistema nervioso central::médula espinal [ANATOMÍA], Statistics, Probability and Uncertainty, Medul·la espinal - Malalties - Imatgeria, 030217 neurology & neurosurgery, Information Systems
Abstract

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord., Measurement(s)spinal cordTechnology Type(s)magnetic resonance imagingFactor Type(s)manufacturer • siteSample Characteristic - OrganismHomo sapiensSample Characteristic - LocationCanada • Switzerland • Australia • United States of America • United Kingdom • Germany • French Republic • Czech Republic • Italy • Japan • Kingdom of Spain • China Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14052269

Published
2021
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28. Non-invasive in vivo MRI detects long-term microstructural brain alterations related to learning and memory impairments in a model of inflammation-induced white matter injury

Author

Wyston C, Pierre, Erjun, Zhang, Irène, Londono, Benjamin, De Leener, Frédéric, Lesage, and Gregory A, Lodygensky

Subjects
Inflammation, Lipopolysaccharides, Memory Disorders, Infant, Newborn, Brain, Magnetic Resonance Imaging, White Matter, Rats, Behavioral Neuroscience, Diffusion Tensor Imaging, Pregnancy, Brain Injuries, Animals, Humans, Female, Rats, Wistar, Infant, Premature
Abstract

Magnetic resonance imaging (MRI) is currently under investigation as a non-invasive tool to monitor neurodevelopmental trajectories and predict risk of cognitive deficits following white matter injury (WMI) in very preterm infants. In the present study, we evaluated the capacity of multimodal MRI (high-resolution T2-weighted imaging and diffusion tensor imaging)to assess changes following WMI and their relationship to learning and memory performance in Wistar rats as it has been demonstrated for preterm infants. Multimodal MRI performed at P31-P32 shown that animals exposed to neonatal LPS could be classified into two groups: minimal and overt injury. Animals with overt injury had significantly enlarged ventricles, hippocampal atrophy, diffusivity changes in hippocampal white and gray matter, in the striatum and the cortex. Following neonatal LPS exposure, animals presented learning and memory impairments as shown at the fear conditioning test at P36-P38. The severity of learning and memory deficits was related to increased mean diffusivity in the hippocampal region. In conclusion, non-invasive multimodal MRI (volumetric and DTI) assessed and classified the extent of injury at long-term following neonatal LPS exposure. Microstructural changes in the hippocampus at DTI were associated to learning and memory impairments. This further highlights the utility of multimodal MRI as a non-invasive quantitative biomarker following perinatal inflammation.

Published
2022
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29. Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Author

Ferran Prados, Alex Rovira, Ali Khatibi, Nico Papinutto, George Tackley, Maria Marcella Laganà, Akifumi Hagiwara, Kristin P. O’Grady, René Labounek, Nawal Kinany, Joo Won Kim, Loan Mattera, Christine S. Law, Seth A. Smith, Daniel Papp, Todd B. Parrish, Julien Cohen-Adad, Robert L. Barry, Nyoman D. Kurniawan, Sara Llufriu, Giancarlo Germani, Slawomir Kusmia, Miloš Keřkovský, Eloy Martinez-Heras, Matthew D. Budde, Richard G. Wise, Claudia A. M. Wheeler-Kingshott, P Wyss, Rebecca S. Samson, Karla R. Epperson, Y. Suzuki, Haleh Karbasforoushan, Issei Fukunaga, Igor Nestrasil, Virginie Callot, Maxime Descoteaux, Yazhuo Kong, Paul Kuntke, Kenneth A. Weber, Alexandru Foias, Marco Battiston, Hagen H. Kitzler, Charley Gros, Giovanni Savini, Michela Fratini, Guillaume Gilbert, Alan C. Seifert, Anna J.E. Combes, Carina Arneitz, Julien Doyon, Tobias Leutritz, Zachary A. Smith, Laura Barlow, James M. Joers, Kevin S. Epperson, Pierre-Gilles Henry, Markus Barth, Francesco Grussu, Deborah Pareto, Christophe Lenglet, Jürgen Finsterbusch, Shannon H. Kolind, Christian Büchel, Sean Mackey, Marios C. Yiannakas, Petr Kudlička, Mihael Abramovic, Benjamin De Leener, Elisabeth Solana, Anna Pichiecchio, Eva Alonso-Ortiz, Nikolaus Weiskopf, Kouhei Kamiya, Alexandra Tinnermann, Jan Valošek, Patrick Freund, Junqian Xu, Marek Dostál, Dimitri Van De Ville, Alex K. Smith, Federico Giove, Marc J. Ruitenberg, Falk Eippert, Nicole Atcheson, Paulo Loureiro de Sousa, Tomáš Horák, Masaaki Hori, Yaou Liu, Maryam Seif, Adam V. Dvorak, and Cornelia Laule

Subjects
Adult, Male, Statistics and Probability, Data descriptor, Science, MEDLINE, Spinal cord diseases, Neuroimaging, Spinal Cord Diseases, Imaging techniques, Library and Information Sciences, GeneralLiterature_MISCELLANEOUS, Education, Databases, Text mining, Image Processing, Computer-Assisted, Humans, Medicine, Author Correction, Reproducibility, business.industry, MT protocol, Reproducibility of Results, Spinal cord, Magnetic Resonance Imaging, Computer Science Applications, medicine.anatomical_structure, Spinal Cord, Female, Statistics, Probability and Uncertainty, business, Cartography, Information Systems
Abstract

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

Published
2021

30. Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis

Author

Marcel Warntjes, Russell Ouellette, Michael Platten, Caterina Mainero, Tobias Granberg, Nikola Stikov, Gabriel Mangeat, Julien Cohen-Adad, Maxime Wabartha, Virginija Danylaité Karrenbauer, and Benjamin De Leener

Subjects
Adult, Male, Multiple Sclerosis, Neurologi, Differential diagnosis, hereditary diffuse leukoencephalopathy with spheroids, multiple sclerosis, quantitative MRI, Machine learning, computer.software_genre, 030218 nuclear medicine & medical imaging, Scan time, Diagnosis, Differential, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Leukoencephalopathies, Healthy control, medicine, Brain mri, Humans, Radiology, Nuclear Medicine and imaging, Aged, business.industry, Multiple sclerosis, Leukodystrophy, Brain, Middle Aged, medicine.disease, Magnetic Resonance Imaging, Confidence interval, 3. Good health, Neurology, Hereditary diffuse leukoencephalopathy with spheroids, Female, Neurology (clinical), Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis. Funding agencies: Stockholm City Council and Karolinska Institutet [ALF 20120213 and 20150166], the Canada Research Chair in Quantitative Magnetic Resonance Imaging [950-230815], the Canadian Institute of Health Research [CIHR FDN-143263], the Canada Foundation for Innovation [32454, 34824], the Fonds de Recherche du Qu´ebec - Sant´e [28826], the Fonds de Recherche du Qu´ebec- Nature et Technologies [2015-PR-182754], the Natural Sciences and Engineering Research Council of Canada [RGPIN-2019-07244], the Canada First Research Excellence Fund (IVADO and TransMedTech), the Courtois NeuroMod project, and the Quebec BioImaging Network [5886, 35450]. Mr.Gabriel Mangeat was supported by the NSERC Alexander Graham Bell Canada Graduate Scholarship and the MITACS Globalink Research Internshipprogram. Dr. Virginija Danylait´e Karrenbauer received the support from Stockholm County Council [ALF medicine 20160457]; Biogen (recipient of grantand scholarship, PI for project sponsored by); Novartis (recipient of scholarship and lecture honoraria); and Merck (Scientific Advisory Board member,recipient of lecture honorar ia). Dr. Granberg was supported by Christer Lindgrens and Eva Fredholms’ foundation; Stockholm County Council [ALFmedicine 20170036, ALF postdoc 20180660]; and the Swedish Society for Medical Research (Postdoctoral research fellowship, Big grant).

Published
2020

31. Topologically preserving straightening of spinal cord MRI

Author

Julien Cohen-Adad, Gabriel Mangeat, Benjamin De Leener, Allan R. Martin, Nikola Stikov, Michael G. Fehlings, Virginie Callot, and Sara M. Dupont

Subjects
education.field_of_study, medicine.diagnostic_test, Computer science, Population, Magnetic resonance imaging, Anatomy, medicine.disease, Spinal cord, computer.software_genre, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Myelopathy, 0302 clinical medicine, medicine.anatomical_structure, Spinal cord compression, Robustness (computer science), Voxel, medicine, Radiology, Nuclear Medicine and imaging, Segmentation, education, computer, 030217 neurology & neurosurgery, Biomedical engineering
Abstract

Purpose To propose a robust and accurate method for straightening magnetic resonance (MR) images of the spinal cord, based on spinal cord segmentation, that preserves spinal cord topology and that works for any MRI contrast, in a context of spinal cord template-based analysis. Materials and Methods The spinal cord curvature was computed using an iterative Non-Uniform Rational B-Spline (NURBS) approximation. Forward and inverse deformation fields for straightening were computed by solving analytically the straightening equations for each image voxel. Computational speed-up was accomplished by solving all voxel equation systems as one single system. Straightening accuracy (mean and maximum distance from straight line), computational time, and robustness to spinal cord length was evaluated using the proposed and the standard straightening method (label-based spline deformation) on 3T T2- and T1-weighted images from 57 healthy subjects and 33 patients with spinal cord compression due to degenerative cervical myelopathy (DCM). Results The proposed algorithm was more accurate, more robust, and faster than the standard method (mean distance = 0.80 vs. 0.83 mm, maximum distance = 1.49 vs. 1.78 mm, time = 71 vs. 174 sec for the healthy population and mean distance = 0.65 vs. 0.68 mm, maximum distance = 1.28 vs. 1.55 mm, time = 32 vs. 60 sec for the DCM population). Conclusion A novel image straightening method that enables template-based analysis of quantitative spinal cord MRI data is introduced. This algorithm works for any MRI contrast and was validated on healthy and patient populations. The presented method is implemented in the Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1209–1219.

Published
2017
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32. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data

Author

Nikola Stikov, Julien Cohen-Adad, Sara M. Dupont, Virginie Callot, Benjamin De Leener, Simon Lévy, Vladimir S. Fonov, D. Louis Collins, École Polytechnique de Montréal (EPM), McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], 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), Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), and Université de Montréal (UdeM)

Subjects
medicine.medical_specialty, Computer science, Cognitive Neuroscience, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Atlas (anatomy), Spondylotic myelopathy, Image Processing, Computer-Assisted, medicine, Humans, Medical physics, Spinal cord injury, open-source, medicine.diagnostic_test, Template, Multiple sclerosis, Magnetic resonance imaging, medicine.disease, Spinal cord, Magnetic Resonance Imaging, Pipeline (software), Toolbox, medicine.anatomical_structure, Spinal Cord, Neurology, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Atlas, Neuroscience, Software, 030217 neurology & neurosurgery, MRI
Abstract

International audience; For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures.

Published
2017
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33. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord

Author

Kouhei Kamiya, Massimo Filippi, Marios C. Yiannakas, Ferran Prados, Rohit Bakshi, Hugh Kearney, Sridar Narayanan, Pierre Labauge, Timothy M. Shepherd, Jean-Christophe Brisset, Russell Ouellette, Tobias Granberg, Ren Zhuoquiong, Caterina Mainero, Julien Cohen-Adad, Atef Badji, Jean Pelletier, Josefina Maranzano, Erik Charlson, Leszek Stawiarz, Seth A. Smith, Yasuhiko Tachibana, Yaou Liu, Charley Gros, Masaaki Hori, Dominique Eden, Olga Ciccarelli, Virginie Callot, Daniel S. Reich, Jennifer Lefeuvre, Gilles Edan, Sara M. Dupont, Govind Nair, Henitsoa Rasoanandrianina, Elise Bannier, Paola Valsasina, Jason F. Talbott, Shahamat Tauhid, Jan Hillert, Lydia Chougar, Anne Kerbrat, Constantina A. Treaba, Maria A. Rocca, Benjamin De Leener, Bertrand Audoin, École Polytechnique de Montréal (EPM), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Xuanwu Hospital of Capital, Deutsche Telekom Group, Department of Computer Science [Liverpool], University of Liverpool, Karolinska Institutet [Stockholm], Department of Radiology and Biomedical Imaging [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), 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), Service de Neurologie [Rennes] = Neurology [Rennes], CHU Pontchaillou [Rennes], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), 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), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-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)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), Translational imaging group [London] (TIG), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL)-University College of London [London] (UCL)-Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Vanderbilt University [Nashville], Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), National Institute of Radiological Sciences (NIRS), Juntendo University Hospital [Tokyo], McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Funded by the Canada Research Chair in Quantitative Magnetic Resonance Imaging, CIHR-FDN-143263, FRQS-28826, FRQNT-2015-PR-182754, NSERC-435897–2013, QBIN, NMSS RG-1501–02840 (S.A.S.), NIH/NINDS R21 NS087465–01 (S.A.S.), NIH/NEI R01EY023240 (S.A.S.), DoD W81XWH-13–0073 (S.A.S.), the Intramural Research Program of NIH/NINDS (J.L., D.S.R., G.N.), PHRC (EMISEP), NIH/NINDS R01NS078322–02 (C.M.), ClinicalTrials.gov (NCT02117375), CNRS, 'Fondation A*midex-Investissements d’Avenir', the Stockholm County Council (ALF grant 20150166), Swedish Society for Medical Research (T.G.), Guarantors of Brain (F.P.), ANR-10-COHO-002, OFSEP, EDMUS Foundation against multiple sclerosis, UK MS Society and the National Institute for Health Research University College London Hospitals Biomedical Research Centre, EMD Serono (C.M., R.B.)., ANR-10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Bannier, Elise, Cohortes - Observatoire Français de la Sclérose en Plaques - - OFSEP2010 - ANR-10-COHO-0002 - COHO - VALID, University of California [San Francisco] (UCSF), University of California-University of California, 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), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Eden, Dominique, Gros, Charley, Badji, Atef, Dupont, Sara M, De Leener, Benjamin, Maranzano, Josefina, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobia, Ouellette, Russell, Stawiarz, Leszek, Hillert, Jan, Talbott, Jason, Kerbrat, Anne, Edan, Gille, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A, Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Mario, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth A, Andrada Treaba, Constantina, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S, Nair, Govind, Shepherd, Timothy M, Charlson, Erik, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
0301 basic medicine, Adult, Male, medicine.medical_specialty, Cord, multicentre, Grey matter, multiple sclerosis, Spinal Cord Diseases, lesions, White matter, Lesion, 03 medical and health sciences, Disability Evaluation, 0302 clinical medicine, Multiple Sclerosis, Relapsing-Remitting, medicine, Humans, Gray Matter, 10. No inequality, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Spatial Analysis, Expanded Disability Status Scale, Clinically isolated syndrome, business.industry, Multiple sclerosis, Brain, Cervical Cord, spinal cord, Original Articles, Middle Aged, Multiple Sclerosis, Chronic Progressive, medicine.disease, Spinal cord, Magnetic Resonance Imaging, White Matter, 030104 developmental biology, medicine.anatomical_structure, Disease Progression, Female, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Neurology (clinical), Radiology, medicine.symptom, business, 030217 neurology & neurosurgery, MRI
Abstract

International audience; Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

Published
2019
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34. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Author

Vincent Auclair, Ferran Prados, Jean-Christophe Brisset, Josefina Maranzano, Sridar Narayanan, Kouhei Kamiya, Constantina A. Treaba, Atef Badji, Hugh Kearney, Jean Pelletier, Pierre Labauge, Elise Bannier, Paola Valsasina, Erik Charlson, Daniel S. Reich, Olga Ciccarelli, Dominique Eden, Ren Zhuoquiong, Henitsoa Rasoanandrianina, Virginie Callot, Gilles Edan, Seth A. Smith, Marios C. Yiannakas, Jennifer Lefeuvre, Govind Nair, Allan R. Martin, Julien Cohen-Adad, Timothy M. Shepherd, Jason F. Talbott, Donald G. McLaren, Charley Gros, Masaaki Hori, Shahabeddin Vahdat, Julien Doyon, Yasuhiko Tachibana, Leszek Stawiarz, Shahamat Tauhid, Jan Hillert, Massimo Filippi, Lydia Chougar, Anne Kerbrat, Russell Ouellette, Caterina Mainero, Sara M. Dupont, Tobias Granberg, Bertrand Audoin, Ali Khatibi, Rohit Bakshi, Maria A. Rocca, Michael G. Fehlings, Benjamin De Leener, Yaou Liu, École Polytechnique de Montréal (EPM), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Department of Radiology and Biomedical Imaging [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), Xuanwu Hospital of Capital, Deutsche Telekom Group, Department of Computer Science [Liverpool], University of Liverpool, National Institute of Radiological Sciences (NIRS), Juntendo University Hospital [Tokyo], Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Karolinska Institutet [Stockholm], CHU Pontchaillou [Rennes], 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), Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [CHU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université de Montpellier (UM), 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), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-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)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), NMR Research Unit [London], Institute of Neurology [London], University College of London [London] (UCL)-University College of London [London] (UCL), University College of London [London] (UCL), Vanderbilt University [Nashville], Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), Biospective [Montréal], University of Toronto, Unité de Neuroimagerie Fonctionnelle [Montréal] (UNF-CRIUGM), Université de Montréal (UdeM)-Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Stanford University, McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), CIHR FDN-143263, Canadian Institutes of Health Research, International Society of Regulatory Toxicology and Pharmacology, Svenska Sällskapet för Medicinsk Forskning, IVADO, EDMUS Foundation, TransMedTech, R01 EY023240 (SAS), NIH/NEI, RG-1501-02840 (SAS), National Multiple Sclerosis Society, Intramural Research Program, Hospital Programme of Clinical Research (PHRC), NCT02117375, EMISEP project, Fondation A*midex-Investissements d'Avenir, 435897-2013, Natural Sciences and Engineering Research Council of Canada, Observatoire Français de la Sclérose en plaques (OFSEP), 13034, MOP, Canada Research Chairs, National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), 2015-PR-182754, Fonds de Recherche du Québec - Nature et Technologies, W81XWH-13-0073 (SAS), U.S. Department of Defense, ANR-10-COHO-002, Agence Nationale de la Recherche, R21 NS087465-01 (SAS), National Institute of Neurological Disorders and Stroke, Fondation Aix-Marseille Universite, SensoriMotor Rehabilitation Research Team (SMRRT), Wings for Life, 20150166, Stockholms Läns Landsting, Centre National de la Recherche Scientifique, 28826, Fonds de Recherche du Québec - Santé, 32454, Canada Foundation for Innovation, Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), University of California [San Francisco] (UCSF), University of California-University of California, SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), 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 Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital Gui de Chauliac [Montpellier]-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université Montpellier 1 (UM1)-Université de Montpellier (UM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Gros, Charley, De Leener, Benjamin, Badji, Atef, Maranzano, Josefina, Eden, Dominique, Dupont, Sara M., Talbott, Jason, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobia, Ouellette, Russell, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Stawiarz, Leszek, Hillert, Jan, Bannier, Elise, Kerbrat, Anne, Edan, Gille, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A., Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Mario, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth, Treaba, Constantina Andrada, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S., Nair, Govind, Auclair, Vincent, Mclaren, Donald G., Martin, Allan R., Fehlings, Michael G., Vahdat, Shahabeddin, Khatibi, Ali, Doyon, Julien, Shepherd, Timothy, Charlson, Erik, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
FOS: Computer and information sciences, Image Processing, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Neurodegenerative, computer.software_genre, Convolutional neural network, Medical and Health Sciences, Pattern Recognition, Automated, 0302 clinical medicine, Computer-Assisted, Segmentation, Voxel, Image Processing, Computer-Assisted, Medicine, Spinal Cord Injury, Observer Variation, Spinal cord, medicine.diagnostic_test, 05 social sciences, Magnetic Resonance Imaging, 3. Good health, medicine.anatomical_structure, Neurology, Neurological, Biomedical Imaging, Convolutional neural networks, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Radiology, medicine.symptom, MRI, Automated, medicine.medical_specialty, Cord, Multiple Sclerosis, Physical Injury - Accidents and Adverse Effects, Neural Networks, Cognitive Neuroscience, Bioengineering, Pattern Recognition, Sensitivity and Specificity, Autoimmune Disease, 050105 experimental psychology, Article, Lesion, Multiple sclerosis, 03 medical and health sciences, Computer, Humans, 0501 psychology and cognitive sciences, Multiple sclerosi, Traumatic Head and Spine Injury, Neurology & Neurosurgery, business.industry, Psychology and Cognitive Sciences, Neurosciences, Reproducibility of Results, Magnetic resonance imaging, medicine.disease, Brain Disorders, Neural Networks, Computer, business, computer, 030217 neurology & neurosurgery
Abstract

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg, a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. The proposed framework is open-source and readily available in the Spinal Cord Toolbox., Comment: 38 pages, 7 figures, 2 tables

Published
2019
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35. Can microstructural MRI detect subclinical tissue injury in subjects with asymptomatic cervical spinal cord compression? A prospective cohort study

Author

David W. Cadotte, David J. Mikulis, Michael G. Fehlings, Julien Cohen-Adad, Aria Nouri, Howard J. Ginsberg, Benjamin De Leener, Lindsay Tetreault, Allan R. Martin, Jefferson R. Wilson, and Adrian Crawley

Subjects
Male, magnetization transfer, Neurodegenerative, 030218 nuclear medicine & medical imaging, Myelopathy, 0302 clinical medicine, preclinical, Longitudinal Studies, Prospective Studies, Prospective cohort study, Spinal Cord Injury, Spinal cord injury, medicine.diagnostic_test, General Medicine, Middle Aged, diffusion tensor imaging, Magnetic Resonance Imaging, quantitative mri, 3. Good health, medicine.anatomical_structure, Spinal Cord, Neurology, Neurological, Public Health and Health Services, Cervical Vertebrae, Female, Radiology, medicine.symptom, Adult, medicine.medical_specialty, Physical Injury - Accidents and Adverse Effects, Clinical Sciences, Grey matter, Asymptomatic, Spinal Cord Diseases, 03 medical and health sciences, myelopathy, Spinal cord compression, Clinical Research, medicine, Humans, Traumatic Head and Spine Injury, Other Medical and Health Sciences, business.industry, Prevention, Research, Neurosciences, Magnetic resonance imaging, medicine.disease, Spinal cord, spinal cord injury, business, Spinal Cord Compression, 030217 neurology & neurosurgery
Abstract

Objectives Degenerative cervical myelopathy (DCM) involves extrinsic spinal cord compression causing tissue injury and neurological dysfunction. Asymptomatic spinal cord compression (ASCC) is more common, but its significance is poorly defined. This study investigates if: (1) ASCC can be automatically diagnosed using spinal cord shape analysis; (2) multiparametric quantitative MRI can detect similar spinal cord tissue injury as previously observed in DCM. Design Prospective observational longitudinal cohort study. Setting Single centre, tertiary care and research institution. Participants 40 neurologically intact subjects (19 female, 21 male) divided into groups with and without ASCC. Interventions None. Outcome measures Clinical assessments: modified Japanese Orthopaedic Association score and physical examination. 3T MRI assessments: automated morphometric analysis compared with consensus ratings of spinal cord compression, and measures of tissue injury: cross-sectional area, diffusion fractional anisotropy, magnetisation transfer ratio and T2*-weighted imaging white to grey matter signal intensity ratio (T2*WI WM/GM) extracted from rostral (C1–3), caudal (C6–7) and maximally compressed levels. Results ASCC was present in 20/40 subjects. Diagnosis with automated shape analysis showed area under the curve >97%. Five MRI metrics showed differences suggestive of tissue injury in ASCC compared with uncompressed subjects (p

Published
2018

36. Automatic spinal cord localization, robust to MRI contrasts using global curve optimization

Author

Sara M. Dupont, Michaël Sdika, Vincent Auclair, Virginie Callot, Benjamin De Leener, Charley Gros, Subhash Tummala, Julien Cohen-Adad, Allan R. Martin, Rohit Bakshi, Michael G. Fehlings, Donald G. McLaren, École Polytechnique de Montréal (EPM), Division of Neurosurgery, Department of Surgery, University of Toronto, Brigham and Women's Hospital [Boston], Biospective [Montréal], Centre de résonance magnétique biologique et médicale (CRMBM), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Service Informatique et développements, 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), Images et Modèles, Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), 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), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-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)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], 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), and 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)

Subjects
Mean squared error, Wilcoxon signed-rank test, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Health Informatics, Image processing, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, Hough transform, law.invention, 03 medical and health sciences, 0302 clinical medicine, Segmentation, law, Machine learning, medicine, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Balance (ability), Radiological and Ultrasound Technology, business.industry, Reproducibility of Results, Pattern recognition, Spinal cord, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, Detection, medicine.anatomical_structure, Spinal Cord, Feature (computer vision), Global optimization, Computer Vision and Pattern Recognition, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Algorithms, MRI
Abstract

International audience; During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced.The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as “OptiC”, from a large dataset involving 20 centers, 4 contrasts (T2-weighted n = 287, T1-weighted n = 120, T2∗-weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions.Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state-of-the-art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed-rank test p-value

Published
2018
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37. Monitoring for myelopathic progression with multiparametric quantitative MRI

Author

Sukhvinder Kalsi-Ryan, David J. Mikulis, Michael G. Fehlings, David W. Cadotte, Julien Cohen-Adad, Howard J. Ginsberg, Lindsay Tetreault, Adrian Crawley, Benjamin De Leener, Eric M. Massicotte, Allan R. Martin, Jefferson R. Wilson, Aria Nouri, and Toft, Mathias

Subjects
Male, Physiology, Image Processing, Social Sciences, lcsh:Medicine, Hands, Walking, Diagnostic Radiology, 030218 nuclear medicine & medical imaging, Myelopathy, Cognition, 0302 clinical medicine, Computer-Assisted, Medicine and Health Sciences, Image Processing, Computer-Assisted, Psychology, Longitudinal Studies, lcsh:Science, Musculoskeletal System, Brain Mapping, Multidisciplinary, medicine.diagnostic_test, Radiology and Imaging, Disease Management, Middle Aged, Magnetic Resonance Imaging, Arms, Diffusion Tensor Imaging, medicine.anatomical_structure, Disease Progression, Biomedical Imaging, Sensory Perception, Female, Radiology, Anatomy, Gait Analysis, Algorithms, Research Article, medicine.medical_specialty, Imaging Techniques, General Science & Technology, Brain Morphometry, Decision Making, Clinical Decision-Making, Surgical and Invasive Medical Procedures, Neuroimaging, Grey matter, Research and Analysis Methods, Spinal Cord Diseases, 03 medical and health sciences, Atrophy, Diagnostic Medicine, Clinical Research, Fractional anisotropy, medicine, Humans, Magnetization transfer, Aged, Biological Locomotion, business.industry, Limbs (Anatomy), lcsh:R, Cognitive Psychology, Biology and Life Sciences, Magnetic resonance imaging, medicine.disease, Orthopedic surgery, Cognitive Science, lcsh:Q, business, 030217 neurology & neurosurgery, Neuroscience, Diffusion MRI, Follow-Up Studies
Abstract

Background Patients with mild degenerative cervical myelopathy (DCM) are often managed non-operatively, and surgery is recommended if neurological progression occurs. However, detection of progression is often subjective. Quantitative MRI (qMRI) directly measures spinal cord (SC) tissue changes, detecting axonal injury, demyelination, and atrophy. This longitudinal study compared multiparametric qMRI with clinical measures of progression in non-operative DCM patients. Methods 26 DCM patients were followed. Clinical data included modified Japanese Orthopedic Association (mJOA) and additional assessments. 3T qMRI data included cross sectional area, diffusion fractional anisotropy, magnetization transfer ratio, and T2*-weighted white/grey matter signal ratio, extracted from the compressed SC and above/below. Progression was defined as 1) patients’ subjective impression, 2) 2-point mJOA decrease, 3) ≥3 clinical measures worsening ≥5%, 4) increased compression on MRI, or 5) ≥1 of 10 qMRI measures or composite score worsening (p < 0.004, corrected). Results Follow-up (13.5 ± 4.9 months) included mJOA in all 26 patients, MRI in 25, and clinical/qMRI in 22. 42.3% reported subjective worsening, compared with mJOA (11.5%), MRI (20%), comprehensive assessments (54.6%), and qMRI (68.2%). Relative to subjective worsening, qMRI showed 100% sensitivity and 53.3% specificity compared with comprehensive assessments (75%, 60%), mJOA (27.3%, 100%), and MRI (18.2%, 81.3%). A decision-making algorithm incorporating qMRI identified progression and recommended surgery for 11 subjects (42.3%). Conclusions Quantitative MRI shows high sensitivity to detect myelopathic progression. Our results suggest that neuroplasticity and behavioural adaptation may mask progressive SC tissue injury. qMRI appears to be a useful method to confirm subtle myelopathic progression in individual patients, representing an advance toward clinical translation of qMRI.

Published
2018

38. PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space

Author

Julien Cohen-Adad, Nikola Stikov, D. Louis Collins, Virginie Callot, Vladimir S. Fonov, Benjamin De Leener, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], 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), École Polytechnique de Montréal (EPM), Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), and Université de Montréal (UdeM)

Subjects
Adult, Male, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cognitive Neuroscience, 030218 nuclear medicine & medical imaging, White matter, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Atlases as Topic, Atlas (anatomy), medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Image Processing, Computer-Assisted, Humans, ICBM, snc, Entire spinal cord, Template, Healthy subjects, Anatomy, Spinal cord, Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, Spinal Cord, Female, Brainstem, Atlas, 030217 neurology & neurosurgery, Software, MRI, Brain Stem
Abstract

International audience; Template-based analysis of multi-parametric MRI data of the spinal cord sets the foundation for standardization and reproducibility, thereby helping the discovery of new biomarkers of spinal-related diseases. While MRI templates of the spinal cord have been recently introduced, none of them cover the entire spinal cord. In this study, we introduced an unbiased multimodal MRI template of the spinal cord and the brainstem, called PAM50, which is anatomically compatible with the ICBM152 brain template and uses the same coordinate system. The PAM50 template is based on 50 healthy subjects, covers the full spinal cord (C1 to L2 vertebral levels) and the brainstem, is available for T1-, T2-and T2*-weighted MRI contrasts and includes a probabilistic atlas of the gray matter and white matter tracts. Template creation accuracy was assessed by computing the mean and maximum distance error between each individual spinal cord centerline and the PAM50 centerline, after registration to the template. Results showed high accuracy for both T1- (mean = 0.37 ± 0.06 mm; max = 1.39 ± 0.58 mm) and T2-weighted (mean = 0.11 ± 0.03 mm; max = 0.71 ± 0.27 mm) contrasts. Additionally, the preservation of the spinal cord topology during the template creation process was verified by comparing the cross-sectional area (CSA) profile, averaged over all subjects, and the CSA profile of the PAM50 template. The fusion of the PAM50 and ICBM152 templates will facilitate group and multi-center studies of combined brain and spinal cord MRI, and enable the use of existing atlases of the brainstem compatible with the ICBM space.

Published
2017
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39. OptiC: Robust and Automatic Spinal Cord Localization on a Large Variety of MRI Data Using a Distance Transform Based Global Optimization

Author

Michael G. Fehlings, Charley Gros, Subhash Tummala, Virginie Callot, Vincent Auclair, Benjamin De Leener, Donald G. McLaren, Allan R. Martin, Julien Cohen-Adad, Rohit Bakshi, Sara M. Dupont, Michaël Sdika, École Polytechnique de Montréal (EPM), Division of Neurosurgery, Department of Surgery, University of Toronto, Brigham and Women's Hospital [Boston], Biospective [Montréal], 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), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-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)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), 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)-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)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Service Informatique et développements, Functional Neuroimaging Unit, CRIUGM, Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), Université de Montréal (UdeM)-Université de Montréal (UdeM), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), 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, Population, computer.software_genre, 030218 nuclear medicine & medical imaging, Hough transform, law.invention, 03 medical and health sciences, Segmentation, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Voxel, law, Histogram, Machine learning, medicine, Computer vision, education, Mathematics, Spinal cord, education.field_of_study, business.industry, Support vector machine, Detection, medicine.anatomical_structure, Localization, Global optimization, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computer, Distance transform, 030217 neurology & neurosurgery, MRI
Abstract

International audience; Localizing the center of the spinal cord on MR images is a critical step toward fully automated and robust quantitative analysis, which is essential to achieve clinical utilization. While automatic localization of the spinal cord might appear as a simple task, that has already been addressed extensively, it is much more challenging to achieve this across the large variation in MRI contrasts, field of view, resolutions and pathologies. In this study, we introduce a novel method, called “OptiC”, to automatically and robustly localize the spinal cord on a large variety of MRI data. Starting from a localization map computed by a linear Support Vector Machine trained with Histogram of Oriented Gradient features, the center of the spinal cord is localized by solving an optimization problem, that introduces a trade-off between the localization map and the cord continuity along the superior-inferior axis. The OptiC algorithm features an efficient search (with a linear complexity in the number of voxels) and ensures the global minimum is reached. OptiC was compared to a recently-published method based on the Hough transform using a broad range of MRI data, involving 13 different centers, 3 contrasts (T2-weighted n=278, T1-weighted n=112 and T∗2-weighted n=263), with a total of 441 subjects, including 133 patients with traumatic and neurodegenerative diseases. Overall, OptiC was able to find 98.5% of the gold-standard centerline coverage, with a mean square error of 1.21 mm, suggesting that OptiC could reliably be used for subsequent analyses tasks, such as cord segmentation, opening the door to more robust analysis in patient population.

Published
2017
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40. Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections

Author

Youngjin Yoo, Tom Brosch, Lisa Tang, Roger Tam, Julien Cohen-Adad, Emil Ljungberg, Benjamin De Leener, Adam Porisky, and Anthony Traboulsee

Subjects
Jaccard index, Computer science, business.industry, Deep learning, Pattern recognition, Filter (signal processing), Grey matter, Spinal cord, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Test set, medicine, Segmentation, Artificial intelligence, business, Encoder, Neuroscience, 030217 neurology & neurosurgery
Abstract

Segmentation of grey matter in magnetic resonance images of the spinal cord is an important step in assessing disease state in neurological disorders such as multiple sclerosis. However, manual delineation of spinal cord tissue is time-consuming and susceptible to variability introduced by the rater. We present a novel segmentation method for spinal cord tissue that uses fully convolutional encoder networks (CENs) for direct end-to-end training and includes shortcut connections to combine multi-scale features, similar to a u-net. While CENs with shortcuts have been used successfully for brain tissue segmentation, spinal cord images have very different features, and therefore deserve their own investigation. In particular, we develop the methodology by evaluating the impact of the number of layers, filter sizes, and shortcuts on segmentation accuracy in standard-resolution cord MRIs. This deep learning-based method is trained on data from a recent public challenge, consisting of 40 MRIs from 4 unique scan sites, with each MRI having 4 manual segmentations from 4 expert raters, resulting in a total of 160 image-label pairs. Performance of the method is evaluated using an independent test set of 40 scans and compared against the challenge results. Using a comprehensive suite of performance metrics, including the Dice similarity coefficient (DSC) and Jaccard index, we found shortcuts to have the strongest impact (0.60 to 0.80 in DSC), while filter size (0.76 to 0.80) and the number of layers (0.77 to 0.80) are also important considerations. Overall, the method is highly competitive with other state-of-the-art methods.

Published
2017
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41. Robust Structured Light Pattern for Use with a Spatial Light Modulator in 3-D Endoscopy

Author

Pierre Lambert, Benjamin De Leener, Benjamin Mertens, Charles Beumier, Alain Delchambre, and Olivier Debeir

Subjects
De Bruijn sequence, Sequence, Spatial light modulator, Computer science, business.industry, Mechanical Engineering, Hamming distance, Subpixel rendering, Segmentation, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, business, Projection (set theory), Instrumentation, Structured light
Abstract

This article introduces a novel structured light pattern designed to be compatible with the spatial light modulator (SLM) projection. The proposed pattern is a De Bruijn-based sequence applied to a combination of continuous and dashed lines for the pattern. The sequence is coded in the period and duty cycles of the dashed lines. It provides 16 different lines which limits to two the required number of dashed lines needed for identification. The segmentation has been made easier by alternating continuous and dashed lines. As required by the use of SLMs, the sequence has been adapted by making it symmetric. It has been improved by guaranteeing a hamming distance equal to two for two successive dashed lines. The implementation on a virtual model has shown that a subpixel accuracy has been achieved. This pattern has been developped for 3-D endoscopy.

Published
2013
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42. Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter

Author

Julien Cohen-Adad, Manuel Taso, Sara M. Dupont, Virginie Callot, Nikola Stikov, Benjamin De Leener, Arnaud Le Troter, Sylvie Nadeau, École Polytechnique de Montréal (EPM), 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), Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), and Université de Montréal (UdeM)

Subjects
Adult, Male, Registration, Cognitive Neuroscience, 030218 nuclear medicine & medical imaging, multi-parametric MRI, Young Adult, 03 medical and health sciences, Atlases as Topic, 0302 clinical medicine, Segmentation, Sørensen–Dice coefficient, crmbm, Image Processing, Computer-Assisted, medicine, Humans, In patient, Gray matter, Spinal cord injury, Spinal Cord Injuries, business.industry, Multiple sclerosis, Pattern recognition, medicine.disease, Spinal cord, White Matter, Diffusion Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, Spinal Cord, Automatic segmentation, Female, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Artificial intelligence, Nuclear medicine, business, Psychology, Gray (horse), Algorithms, 030217 neurology & neurosurgery, Atlas-based analysis
Abstract

The spinal cord white and gray matter can be affected by various pathologies such as multiple sclerosis, amyotrophic lateral sclerosis or trauma. Being able to precisely segment the white and gray matter could help with MR image analysis and hence be useful in further understanding these pathologies, and helping with diagnosis/prognosis and drug development. Up to date, white/gray matter segmentation has mostly been done manually, which is time consuming, induces a bias related to the rater and prevents large-scale multi-center studies. Recently, few methods have been proposed to automatically segment the spinal cord white and gray matter. However, no single method exists that combines the following criteria: (i) fully automatic, (ii) works on various MRI contrasts, (iii) robust towards pathology and (iv) freely available and open source. In this study we propose a multi-atlas based method for the segmentation of the spinal cord white and gray matter that addresses the previous limitations. Moreover, to study the spinal cord morphology, atlas-based approaches are increasingly used. These approaches rely on the registration of a spinal cord template to an MR image, however the registration usually doesn't take into account the spinal cord internal structure and thus lacks accuracy. In this study, we propose a new template registration framework that integrates the white and gray matter segmentation to account for the specific gray matter shape of each individual subject. Validation of segmentation was performed in 24 healthy subjects using T 2 * -weighted images, in 8 healthy subjects using diffusion weighted images (exhibiting inverted white-to-gray matter contrast compared to T2*-weighted), and in 5 patients with spinal cord injury. The template registration was validated in 24 subjects using T2*-weighted data. Results of automatic segmentation on T2*-weighted images was in close correspondence with the manual segmentation (Dice coefficient in the white/gray matter of 0.91/0.71 respectively). Similarly, good results were obtained in data with inverted contrast (diffusion-weighted image) and in patients. When compared to the classical template registration framework, the proposed framework that accounts for gray matter shape significantly improved the quality of the registration (comparing Dice coefficient in gray matter: p=9.5×10−6). While further validation is needed to show the benefits of the new registration framework in large cohorts and in a variety of patients, this study provides a fully-integrated tool for quantitative assessment of white/gray matter morphometry and template-based analysis. All the proposed methods are implemented in the Spinal Cord Toolbox (SCT), an open-source software for processing spinal cord multi-parametric MRI data.

Published
2016
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43. Brainhack: A collaborative workshop for the open neuroscience community

Author

Caroline Froehlich, David H. O’Connor, Robert C. Welsh, Sook-Lei Liew, Katharine Dunlop, Angela R. Laird, Yonggang Shi, Satrajit S. Ghosh, John W. Van Meter, Pierre-Olivier Quirion, Lucina Q. Uddin, R. Matthew Hutchison, Pierre Bellec, Salma Mesmoudi, Christopher J. Cannistraci, Maarten Mennes, Jonathan Downar, Sebastien Dery, Donald G. McLaren, Ariel Rokem, Prantik Kundu, Daniel S. Margulies, Fernando A. Barrios, Alexandre Rosa Franco, Ramon Fraga Pereira, Andrew J. Gerber, B. Nolan Nichols, R. Cameron Craddock, Erick H. Pasaye, Ziad S. Saad, Felipe Meneguzzi, Benjamin De Leener, Thomas J. Grabowski, Sean Hill, Sarael Alcauter, Julien Cohen-Adad, Daniel J. Lurie, Gautam Prasad, Roberto Toro, Ting Xu, John D. Van Horn, Yves Burnod, Anibal Sólon Heinsfeld, Jean-Baptiste Poline, Scott Peltier, Stephen C. Strother, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Child Mind Institute, The Neuro Bureau [Leipzig], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Université de Montréal (UdeM), Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), Silsoe Research Institute (SRI), Biotechnology and Biological Sciences Research Council, Stanford University, Universidad Nacional Autónoma de México (UNAM), Laboratoire d'Imagerie Biomédicale (LIB), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut des Systèmes Complexes - Paris Ile-de-France (ISC-PIF), École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-École polytechnique (X), Icahn School of Medicine at Mount Sinai [New York] (MSSM), École Polytechnique de Montréal (EPM), McGill University = Université McGill [Montréal, Canada], University Health Network, University of Toronto, Pontifícia Universidade Católica do Rio Grande do Sul [Porto Alegre] (PUCRS), New York State Psychiatric Institute, Columbia University [New York], McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), Harvard Medical School [Boston] (HMS), University of Washington [Seattle], International Neuroinformatics Coordinating Facility, Karolinska Institutet [Stockholm], Harvard University [Cambridge], Florida International University [Miami] (FIU), University of Southern California (USC), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Biospective [Montréal], Massachusetts General Hospital [Boston], Donders Institute for Brain, Cognition and Behaviour, Radboud university [Nijmegen], MATRICE Project (ISC-PIF), Sorbonnes university Paris 1, University of Michigan [Ann Arbor], University of Michigan System, The Helen Wills Neuroscience Institute (HWNI), University of California [Berkeley], University of California-University of California, Keck School of Medicine [Los Angeles], eScience Institute Seattle, National Institute of mental health , Bethesda, Rotman Research Institute at the Baycrest Centre (RRI), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Miami University, Miami University [Ohio] (MU), University of Miami Leonard M. Miller School of Medicine (UMMSM), Georgetown University Medical Center, We would also like to thank our sponsors, whose funds have been used to enrich the educational experience at Brainhack and have provided travel support for attendees. These include (in alphabetical order): Allen Institute for Brain Science (OHBM 2013), Amazon Web Services (OHBM 2013, OHBM 2014, Boston 2014, Brainhack AMX 2015), Athinoula A. Martinos Center for Biomedical Imaging (Boston 2014), Child Mind Institute, Inc. (NYC 2014, NYC 2015, MX 2015), FIU Division of Research (Miami 2014), Frontiers (OHBM 2014), Frontiers in Neuroscience (OHBM 2013), International Neuroinformatics Coordinating Facility (OHBM 2013, OHBM 2014, OHBM 2015, MX 2015), MATRICE (Paris 2013), Max Planck Institute for Cognitive and Brain Sciences (Leipzig 2012), Microsoft Azure (OHBM 2015), NIH BD2K Center (1U54EB020406-01) Big Data for Discovery Science (USC, PI: Toga, LA 2015), NIH BD2K Center (1U54EB020403-01) Enigma Center for Worldwide Medicine, Imaging, and Genomics (USC, PI: Thompson, LA 2015), NIH BD2K Supplement for NCANDA (3U01AA021697-04S1) and NCANDA: Data Analysis Component (5U01AA021697-04) (SRI International, PI: Pohl, OHBM2015, MX 2015), Organization for Human Brain Mapping (OHBM 2013, OHBM 2014, OHBM 2015), Ontario Brain Institute (Toronto 2014), Quebec Bio-Imaging Network (MTL 2014, MTL 2015), Siemens (Paris 2013), and University of Miami Flipse Funds (Miami 2015).References, HAL UPMC, Gestionnaire, Biotechnology and Biological Sciences Research Council (BBSRC), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École polytechnique (X)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Pontifical Catholic University of Rio Grande do Sul (PUC-RS), Radboud University [Nijmegen], Université Paris 1 Panthéon-Sorbonne (UP1), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), McGovern Institute for Brain Research at MIT, Ghosh, Satrajit S., Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Harvard University, and ANR-16-EQPX-0003,Matrice - 13 novembre,Matrice - 13 novembre(2016)

Subjects
0301 basic medicine, Open science, Biomedical Research, Computer science, International Cooperation, Health Informatics, Review, Education, 03 medical and health sciences, Networking, 0302 clinical medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Cooperative Behavior, Hackathon, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Neurosciences, 220 Statistical Imaging Neuroscience, Brain, Computational Biology, Congresses as Topic, Unconference, Data science, Collaboration, Research Personnel, Computer Science Applications, Data sharing, 030104 developmental biology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neuroscience, 030217 neurology & neurosurgery
Abstract

Contains fulltext : 167121.pdf (Publisher’s version ) (Open Access) Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.

Published
2016
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44. Segmentation of the human spinal cord

Author

Virginie Callot, Julien Cohen-Adad, Benjamin De Leener, Manuel Taso, École Polytechnique de Montréal (EPM), 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), Centre de recherche de l'Institut universitaire de gériatrie de Montreal (CRIUGM), and Université de Montréal (UdeM)

Subjects
Cord, Biophysics, Spinal Cord Diseases, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, Pattern Recognition, Automated, White matter, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Segmentation, Imaging, Three-Dimensional, Market segmentation, crmbm, Image Interpretation, Computer-Assisted, Medicine, Brain segmentation, Humans, Radiology, Nuclear Medicine and imaging, Gray matter, Radiological and Ultrasound Technology, business.industry, Reproducibility of Results, Anatomy, Spinal cord, Image Enhancement, medicine.anatomical_structure, Spinal Cord, Automatic segmentation, [SDV.IB]Life Sciences [q-bio]/Bioengineering, business, Neuroscience, white matter, 030217 neurology & neurosurgery, Algorithms, MRI
Abstract

International audience; Segmenting the spinal cord contour is a necessary step for quantifying spinal cord atrophy in various diseases. Delineating gray matter (GM) and white matter (WM) is also useful for quantifying GM atrophy or for extracting multiparametric MRI metrics into specific WM tracts. Spinal cord segmentation in clinical research is not as developed as brain segmentation, however with the substantial improvement of MR sequences adapted to spinal cord MR investigations, the field of spinal cord MR segmentation has advanced greatly within the last decade. Segmentation techniques with variable accuracy and degree of complexity have been developed and reported in the literature. In this paper, we review some of the existing methods for cord and WM/GM segmentation, including intensity-based, surface-based, and image-based methods. We also provide recommendations for validating spinal cord segmentation techniques, as it is important to understand the intrinsic characteristics of the methods and to evaluate their performance and limitations. Lastly, we illustrate some applications in the healthy and pathological spinal cord. One conclusion of this review is that robust and automatic segmentation is clinically relevant, as it would allow for longitudinal and group studies free from user bias as well as reproducible multicentric studies in large populations, thereby helping to further our understanding of the spinal cord pathophysiology and to develop new criteria for early detection of subclinical evolution for prognosis prediction and for patient management. Another conclusion is that at the present time, no single method adequately segments the cord and its substructure in all the cases encountered (abnormal intensities, loss of contrast, deformation of the cord, etc.). A combination of different approaches is thus advised for future developments, along with the introduction of probabilistic shape models. Maturation of standardized frameworks, multiplatform availability, inclusion in large suite and data sharing would also ultimately benefit to the community.

Published
2015
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45. A Clinically Feasible Quantitative MRI Protocol to Assess Tissue Injury in the Cervical Spinal Cord Using DTI, MT, and T2*-Weighted Imaging: Reliability and Variations with Confounding Variables

Author

Allan R. Martin, Julien Cohen-Adad, Benjamin De Leener, David W. Cadotte, Michael G. Fehlings, and Izabela Aleksanderek

Subjects
Protocol (science), 030222 orthopedics, medicine.medical_specialty, business.industry, Confounding, Spinal cord, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, medicine, Physical therapy, Surgery, Orthopedics and Sports Medicine, Neurology (clinical), Radiology, T2 weighted, business, 030217 neurology & neurosurgery, Reliability (statistics)
Published
2016
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46. Robust, accurate and fast automatic segmentation of the spinal cord

Author

Samuel Kadoury, Benjamin De Leener, and Julien Cohen-Adad

Subjects
Computer science, Cognitive Neuroscience, Initialization, 030218 nuclear medicine & medical imaging, Hough transform, law.invention, 03 medical and health sciences, 0302 clinical medicine, Cerebrospinal fluid, Atrophy, Robustness (computer science), law, medicine, Image Processing, Computer-Assisted, Humans, Computer vision, Segmentation, Spinal cord injury, Spinal Cord Injuries, Observer Variation, Entire spinal cord, business.industry, Anatomy, medicine.disease, Spinal cord, Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, Spinal Cord, Automatic segmentation, Artificial intelligence, business, 030217 neurology & neurosurgery, Algorithms
Abstract

Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject correspondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. Although several automatic segmentation methods exist, they are often restricted in terms of image contrast and field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, accuracy and speed. The algorithm is based on the propagation of a deformable model and is divided into three parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough transform on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mesh. Secondly, a low-resolution deformable model is propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a local contrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applied on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in 15 healthy subjects and two patients with spinal cord injury, using T1- and T2-weighted images of the entire spinal cord and on multiecho T2*-weighted images. Our method was compared against manual segmentation and against an active surface method. Results show high precision for all the MR sequences. Dice coefficients were 0.9 for the T1- and T2-weighted cohorts and 0.86 for the T2*-weighted images. The proposed method runs in less than 1min on a normal computer and can be used to quantify morphological features such as cross-sectional area along the whole spinal cord.

Published
2013

47. 163 Microstructural MRI Quantifies Tract-Specific Injury and Correlates With Global Disability and Focal Neurological Deficits in Degenerative Cervical Myelopathy

Author

Izabela Aleksanderek, Lindsay Tetreault, Julien Cohen-Adad, Michael G. Fehlings, Sukhvinder Kalsi-Ryan, Howard J. Ginsberg, Adrian P. Crawley, Allan R. Martin, David W. Cadotte, and Benjamin De Leener

Subjects
Spinothalamic tract, Pathology, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Disease progression, Magnetic resonance imaging, medicine.disease, Spinal cord, White matter, 03 medical and health sciences, Myelopathy, 0302 clinical medicine, medicine.anatomical_structure, Medicine, Surgery, 030212 general & internal medicine, Neurology (clinical), Magnetization transfer, business, 030217 neurology & neurosurgery, Diffusion MRI
Abstract

INTRODUCTION:Diffusion tensor imaging (DTI), magnetization transfer (MT), and T2*-weighted imaging measure aspects of spinal cord microstructure. This study investigates if these techniques can quantify injury to individual white matter (WM) tracts and correlate with focal neurological impairments i

Published
2016
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48. Robust structured light pattern for use with a hologram in 3D endoscopy

Author

Olivier Debeir, Benjamin Mertens, Charles Beumier, Alain Delchambre, Pierre Lambert, and Benjamin De Leener

Subjects
De Bruijn sequence, Sequence, business.industry, Computer science, Holography, Hamming distance, Subpixel rendering, law.invention, law, Computer vision, Artificial intelligence, business, Projection (set theory), Hamming code, Structured light
Abstract

This paper introduces a novel structured light pattern designed to be compatible with the hologram projection. The proposed pattern is a De Bruijn-based sequence applied to a combination of continuous and dashed lines for the pattern. The sequence is coded in the period in the period and duty cycles of the dashed lines. It provides 16 different lines which limits to 2 the required number of lines needed for identification. The segmentation has been made easier by alternating continuous and dashed lines. As required by the use of holograms, the sequence has been adapted by making it symmetric. It has been improved by guaranteeing a hamming distance equal to two for two successive dashed lines. The implementation on a virtual model has shown that subpixel accuracy has been achieved. We developped this pattern for 3D endoscopy.

Published
2012
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49. 7 T imaging reveals a gradient in spinal cord lesion distribution in multiple sclerosis

Author

Russell Ouellette, Caterina Mainero, Shahamat Tauhid, Tobias Granberg, Rohit Bakshi, Ambica Mehndiratta, Benjamin De Leener, Eric C. Klawiter, Fawad Yousuf, Valeria Barletta, Elena Herranz, Jacob A. Sloane, Sarah M Dupont, Julien Cohen-Adad, and Constantina A. Treaba

Subjects
0301 basic medicine, Adult, Male, Pathology, medicine.medical_specialty, Grey matter, Ventricular system, White matter, Lesion, 03 medical and health sciences, 0302 clinical medicine, Multiple Sclerosis, Relapsing-Remitting, medicine, Humans, 10. No inequality, Expanded Disability Status Scale, business.industry, Multiple sclerosis, Cervical Cord, Original Articles, Middle Aged, Multiple Sclerosis, Chronic Progressive, Spinal cord, medicine.disease, Magnetic Resonance Imaging, Hyperintensity, 030104 developmental biology, medicine.anatomical_structure, Female, Neurology (clinical), medicine.symptom, business, 030217 neurology & neurosurgery
Abstract

We used 7 T MRI to: (i) characterize the grey and white matter pathology in the cervical spinal cord of patients with early relapsing-remitting and secondary progressive multiple sclerosis; (ii) assess the spinal cord lesion spatial distribution and the hypothesis of an outside-in pathological process possibly driven by CSF-mediated immune cytotoxic factors; and (iii) evaluate the association of spinal cord pathology with brain burden and its contribution to neurological disability. We prospectively recruited 20 relapsing-remitting, 15 secondary progressive multiple sclerosis participants and 11 age-matched healthy control subjects to undergo 7 T imaging of the cervical spinal cord and brain as well as conventional 3 T brain acquisition. Cervical spinal cord imaging at 7 T was used to segment grey and white matter, including lesions therein. Brain imaging at 7 T was used to segment cortical and white matter lesions and 3 T imaging for cortical thickness estimation. Cervical spinal cord lesions were mapped voxel-wise as a function of distance from the inner central canal CSF pool to the outer subpial surface. Similarly, brain white matter lesions were mapped voxel-wise as a function of distance from the ventricular system. Subjects with relapsing-remitting multiple sclerosis showed a greater predominance of spinal cord lesions nearer the outer subpial surface compared to secondary progressive cases. Inversely, secondary progressive participants presented with more centrally located lesions. Within the brain, there was a strong gradient of lesion formation nearest the ventricular system that was most evident in participants with secondary progressive multiple sclerosis. Lesion fractions within the spinal cord grey and white matter were related to the lesion fraction in cerebral white matter. Cortical thinning was the primary determinant of the Expanded Disability Status Scale, white matter lesion fractions in the spinal cord and brain of the 9-Hole Peg Test and cortical thickness and spinal cord grey matter cross-sectional area of the Timed 25-Foot Walk. Spinal cord lesions were localized nearest the subpial surfaces for those with relapsing-remitting and the central canal CSF surface in progressive disease, possibly implying CSF-mediated pathogenic mechanisms in lesion development that may differ between multiple sclerosis subtypes. These findings show that spinal cord lesions involve both grey and white matter from the early multiple sclerosis stages and occur mostly independent from brain pathology. Despite the prevalence of cervical spinal cord lesions and atrophy, brain pathology seems more strongly related to physical disability as measured by the Expanded Disability Status Scale.

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50. Generic acquisition protocol for quantitative MRI of the spinal cord

Author

René Labounek, Nawal Kinany, Michela Fratini, Laura Barlow, Markus Barth, P Wyss, Marco Battiston, Sean Mackey, Nyoman D. Kurniawan, Deborah Pareto, Kenneth A. Weber, Guillaume Gilbert, Robert L. Barry, Anna Pichiecchio, Eva Alonso-Ortiz, Matthew D. Budde, Mihael Abramovic, Alexandra Tinnermann, Giancarlo Germani, Dimitri Van De Ville, Falk Eippert, Anna J.E. Combes, Igor Nestrasil, Marios C. Yiannakas, Haleh Karbasforoushan, Julien Doyon, Cornelia Laule, Nicole Atcheson, Ali Khatibi, Nico Papinutto, Karla R. Epperson, Marek Dostál, Yazhuo Kong, Loan Mattera, Charley Gros, Paulo Loureiro de Sousa, Pierre-Gilles Henry, Alan C. Seifert, Richard G. Wise, Issei Fukunaga, Alexandru Foias, Shannon H. Kolind, Todd B. Parrish, Akifumi Hagiwara, Daniel Papp, Carina Arneitz, Virginie Callot, Christian Büchel, Maria Marcella Laganà, Tobias Leutritz, Slawomir Kusmia, Ferran Prados, Kevin S. Epperson, Marc J. Ruitenberg, Nikolaus Weiskopf, Kouhei Kamiya, Benjamin De Leener, Adam V. Dvorak, Giovanni Savini, Christine S. Law, Petr Kudlička, Paul Kuntke, Julien Cohen-Adad, Alex Rovira, Jan Valošek, Patrick Freund, George Tackley, Zachary A. Smith, Eloy Martinez-Heras, Maxime Descoteaux, Yaou Liu, Miloš Keřkovský, Joo Won Kim, Kristin P. O’Grady, Elisabeth Solana, Rebecca S. Samson, Junqian Xu, Alex K. Smith, Federico Giove, Maryam Seif, Claudia A. M. Wheeler-Kingshott, Tomáš Horák, James M. Joers, Francesco Grussu, Christophe Lenglet, Jürgen Finsterbusch, Sara Llufriu, Hagen H. Kitzler, Masaaki Hori, Yuichi Suzuki, Seth A. Smith, École Polytechnique de Montréal (EPM), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Mila - Quebec AI Institute, Swiss Paraplegic Research = Schweizer Paraplegiker-Stiftung, University of Queensland [Brisbane], University of British Columbia (UBC), Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Massachusetts Institute of Technology (MIT), University College of London [London] (UCL), Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Medical College of Wisconsin [Milwaukee] (MCW), 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), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Vanderbilt University Medical Center [Nashville], Vanderbilt University [Nashville], CHU Sainte Justine [Montréal], Université de Sherbrooke (UdeS), Centre d'Imagerie Moléculaire de Sherbrooke, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Masaryk University and University Hospital Brno, McGill University = Université McGill [Montréal, Canada], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Stanford University School of Medicine [CA, USA], Universität Zürich [Zürich] = University of Zurich (UZH), IRCCS Santa Lucia Foundation, Juntendo University School of Medicine [Tokyo, Japan], University of Pavia, IRCCS Mondino Foundation, Philips Healthcare [Markham], Museo Storico della Fisica e Centro di Studi e Ricerche 'Enrico Fermi', Roma, Vall d'Hebron University Hospital [Barcelona], Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Central European Institute of Technology [Brno] (CEITEC MU), Brno University of Technology [Brno] (BUT), Toho University Omori Medical Center [Tokyo], The University of Tokyo (UTokyo), Northwestern University Feinberg School of Medicine, Stanford University, University of Birmingham [Birmingham], Icahn School of Medicine at Mount Sinai [New York] (MSSM), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva [Switzerland], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Chinese Academy of Sciences [Beijing] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), University of Oxford [Oxford], University of Southern Queensland (USQ), Cardiff University, Epilepsy Society [Buckinghamshire] (MRI Unit), Departments of Ophthalmology and Pediatrics, University of Minnesota, Minneapolis, MN, University of Minnesota [Twin Cities] (UMN), University Hospital Olomouc [Czech Republic], Capital University of Medical Sciences [Beijing] (CUMS), Universitat de Barcelona (UB), University of California [San Francisco] (UCSF), University of California, Feinberg School of Medicine, Northwestern University [Evanston], Universitat Oberta de Catalunya [Barcelona] (UOC), University of Oklahoma Health Sciences Center (OUHSC), Palacky University Olomouc, Leipzig University, University 'G. d'Annunzio' of Chieti-Pescara [Chieti and Pescara, Italy], Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-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)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Masaryk University [Brno] (MUNI), Università degli Studi di Pavia = University of Pavia (UNIPV), Enrico Fermi Center for Study and Research | Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Université de Genève = University of Geneva (UNIGE), University of Oxford, University of California [San Francisco] (UC San Francisco), University of California (UC), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), and Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Adult, Male, medicine.medical_specialty, Computer science, Neuroimaging, Article, General Biochemistry, Genetics and Molecular Biology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Image Processing, Computer-Assisted, medicine, Humans, NeuroPoly, Medical physics, health care economics and organizations, Protocol (science), medicine.diagnostic_test, technology, industry, and agriculture, Healthy subjects, Magnetic resonance imaging, Spinal cord, Magnetic Resonance Imaging, White matter microstructure, humanities, 3. Good health, Acquisition Protocol, medicine.anatomical_structure, Spinal Cord, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030217 neurology & neurosurgery, Diffusion MRI
Abstract

Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.

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