Virginie Callot

This presentation is intended to give a non-exhaustive overview of what can be done in the spinal cord using quantitative MRI. « Classical » sequences that can be robustly used will be described. For each of these sequence families, more advanced techniques will be briefly underlined. Sequences providing functional, metabolic and vascular information will also be discussed. We will finish with a brief overview of recent advances in SC MRI at 7T.

From this « soup » of sequences, attendees should be able to extract the best ingredients and recipes for their own investigation.

Zhiqiang Li¹, Ryan K Robison², Melvyn B Ooi^1,3, and John P Karis^1
1Neuroradiology, Barrow Neurological Institute, Phoenix, AZ, United States, ²Radiology, Phoenix Children's Hospital, Phoenix, AZ, United States, ³Philips Healthcare, Gainesville, FL, United States

Spine is a challenging area for MRI due to both anatomical features and motion. Spiral TSE has been proposed for spinal MRI. Aliasing in spiral spinal MRI is mitigated by combining oversampling with saturation band, with the latter not always achieving consistent and good signal suppression. In this work, the ZOOM technique has been incorporated into spiral TSE. Phantom and in vivo results demonstrate better signal suppression with ZOOM spiral TSE compared to spiral TSE acquired with a saturation band.

Ryan Robison¹, Amber Pokorney¹, Michael Kuwabara¹, and Patricia Cornejo^1
1Phoenix Children's Hospital, Phoenix, AZ, United States

This study evaluates spiral as an alternative to Cartesian in axial T2*-weighted gradient echo imaging of the spine. SNR and CNR measurements were performed on spine data from a healthy volunteer. Expert reviewer ratings were also performed on data from 5 patients. Both the SNR/CNR measurements and reviewer ratings indicate that spiral can yield superior image quality without significant artifacts for a given scan time. 

Alan C Seifert^1,2,3 and S Johanna Vannesjo^4,5
1Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ²Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁴Spinal Cord Injury Center, University Hospital Balgrist, University of Zurich, Zurich, Switzerland, ⁵Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, United Kingdom

BOLD signal in gradient-echo images is a combination of macrovascular and microvascular contributions, where the macrovascular component, arising from larger veins draining the activated tissue, is less specific to the site of activation.  In this work, we image activation produced in the cervical spinal cord by a noxious thermal stimulus at 7T.  We consistently observed activation in the dorsal white matter medial to the dorsal horn, rather than in the gray matter itself. However, due to the relatively straightforward venous architecture of the spinal cord, this observed displaced activation does remain closely related to the true site of neuronal activation.

Benjamin De Leener

Charidimos Tsagkas^1,2,3, Antal Horvath⁴, Alexandra Todea⁵, Jannis Mueller^1,2, Anna Altermatt^2,3, Marina Leimbacher⁶, Simon Pezold⁴, Matthias Weigel^2,4,7, Tanja Haas⁷, Michael Amann^1,3,4, Ludwig Kappos^1,2, Till Sprenger^1,8, Philippe Cattin⁴, Cristina Granziera^1,2,4, and Katrin Parmar^1,2
1Neurologic Clinic and Policlinic, Departments of Medicine, Biomedical Engineering and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland, ²Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland, ³Medical Image Analysis Center (MIAC AG), Basel, Switzerland, ⁴Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland, ⁵Division of Diagnostic and Interventional Neuroradiology, Department of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland, ⁶Medical Faculty, University of Basel, Basel, Switzerland, ⁷Division of Radiological Physics, Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland, ⁸Department of Neurology, DKD Helios Klinik Wiesbaden, Wiesbaden, Germany

Currently, there is no gold-standard for spinal cord (SC) grey and white matter (GM/WM) quantification in multiple sclerosis (MS). In this work, the cervical SC of 24 MS patients and 24 healthy controls (HC) was scanned on a 3T MRI-system using averaged magnetization inversion recovery acquisitions. Manual segmentations were provided to train a “Multi-Dimensional Gated Recurrent Unit” neural network for subsequent automatic SC GM/WM/lesion segmentation. Accuracy of automatic segmentations was high and decreased in the order WM→GM→lesions and HC→MS. MS patients had reduced SC GM and WM compared to HC. Finally, SC GM, WM and lesions correlated with physical disability.

Eva Alonso-Ortiz¹, Charley Gros¹, Alexandru Foias¹, Mihael Abramovic², Christoph Arneitz², Nicole Atcheson³, Laura Barlow⁴, Robert Barry^5,6,7, Markus Barth³, Marco Battiston⁸, Christian Buchel⁹, Matthew Budde¹⁰, Virginie Callot^11,12, Benjamin De Leener^13,14,15, Maxime Descoteaux^16,17, Paulo Loureiro de Sousa¹⁸, Dostal Marek¹⁹, Julien Doyon¹⁵, Adam Dvorak²⁰, Falk Eippert²¹, Karla Epperson²², Jürgen Finsterbusch⁹, Issei Fukunaga²³, Claudia Wheeler-Kingshott^8,24,25, Giancarlo Germani²⁶, Guillaume Gilbert²⁷, Francesco Grussu^28,29, Akifumi Hagiwara²³, Pierre-Gilles Henry³⁰, Tomas Horak³¹, Masaaki Hori²³, James Joers³⁰, K Kamiya³², Haleh Karbasforoushan³³, Ali Khatibi^34,35, Joo-Won Kim³⁶, Nawal Kinany³⁷, Hagen Kitzler³⁸, S Kolind³⁹, Joe Yazhuo Kong^40,41,42, Petr Kudlička³¹, Paul Kuntke⁴³, Nyoman Kurniawan³, Slawomir Kusmia⁴⁴, Rene Labounek^45,46, Maria Marcella Laganà⁴⁷, Corree Laule⁴⁸, Christine Law⁴⁹, Christophe Lenglet³⁰, Tobias Leutritz²¹, Yaou Liu^50,51, Sara Llufriu⁵², Sean Mackey⁵³, Eloy Martinez⁵², Igor Nestrasil^30,45, Nico Papinutto⁵⁴, Daniel Papp⁵⁵, Deborah Pareto⁵⁶, Todd Parrish⁵⁷, Anna Pichiecchio^26,58, Alex Rovira Cañellas⁵⁶, Marc Ruitenberg⁵⁹, Rebecca Samson²⁸, Giorgio Savini²⁶, Maryam Seif⁶⁰, Alan Seifert³⁶, Alex Smith⁵⁵, Z A Smith⁵⁷, Elisabeth Solana⁵², Y Suzuki⁶¹, G Tackley⁴⁴, Alexandra Tinnermann⁹, Jan Valosek⁴⁶, Marios Yiannakas²⁸, Kenneth Weber⁶², Nikolaus Weiskopf²¹, Richard Wise⁴⁴, P O Wyss², Junqian Xu³⁶, and Julien Cohen-Adad^1,63
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada, ²Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland, ³Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, ⁴Department of Radiology, University of British Columbia, Vancouver, BC, Canada, ⁵Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MD, United States, ⁶Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁷Harvard–Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, United States, ⁸Queen Square MS Centre, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, ⁹Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ¹⁰Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States, ¹¹CNRS, CRMBM, Aix-Marseille University, Marseille, France, ¹²APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France, ¹³Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada, ¹⁴CHU Sainte-Justine Research Centre, Montreal, QC, Canada, ¹⁵Montreal Neurological Institute, McGill University, Montreal, QC, Canada, ¹⁶CIMS, Centre de Recherche CHUS, Sherbrooke, QC, Canada, ¹⁷Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department,, Université de Sherbrooke, Sherbrooke, QC, Canada, ¹⁸CNRS, ICube, FMTS, Université de Strasbourg, Strasbourg, France, ¹⁹University Hospital Brno, Brno, Czech Republic, ²⁰Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada, ²¹Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ²²Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, United States, ²³Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan, ²⁴Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ²⁵Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy, ²⁶Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy, ²⁷MR Clinical Science, Philips Healthcare, Markham, ON, Canada, ²⁸Queen Square MS Centre, Queen Square Institute of Neurology, Faculty of Brain Sciences, Faculty of Brain Sciences, University College London, London, United Kingdom, ²⁹Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ³⁰Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, ³¹CEITEC - Central European Institute of Technology, Brno, Czech Republic, ³²University of Tokyo, Tokyo, Japan, ³³Interdepartmental Neuroscience Program, Northwestern University School of Medicine, Chicago, IL, United States, ³⁴Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada, ³⁵Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom, ³⁶Translational and Molecular Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³⁷Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ³⁸Department of Neuroradiology, Technische Universität Dresden, Dresden, Germany, ³⁹Departments of Medicine (Neurology), Physics & Astronomy, Radiology, University of British Columbia, Vancouver, BC, Canada, ⁴⁰CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China, ⁴¹Department of Psychology, University of Chinese Academy of Sciences, Beijing, China, ⁴²Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, ⁴³University Hospital Carl Gustav Carus, Dresden, Germany, ⁴⁴CUBRIC, Cardiff University, Walles, United Kingdom, ⁴⁵Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States, ⁴⁶Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic, ⁴⁷IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy, ⁴⁸Departments of Pathology & Laboratory Medicine, Physics & Astronomy, Radiology; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada, ⁴⁹Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States, ⁵⁰Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, ⁵¹Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China, ⁵²Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain, ⁵³Stanford University School of Medicine, Stanford, CA, United States, ⁵⁴Department of Neurology, University of California San Francisco, San Francisco, CA, United States, ⁵⁵Wellcome Centre For Integrative Neuroimaging, FMRIB, NDCN, University of Oxford, Oxford, United Kingdom, ⁵⁶Neuroradiology Section, Vall Hebron University Hospital, Barcelona, Spain, ⁵⁷Feinberg School of Medicine, Northwestern University School of Medicine, Chicago, IL, United States, ⁵⁸Department of Brain and Behavioural Neuroscience, University of Pavia, Pavia, Italy, ⁵⁹School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia, ⁶⁰Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland, ⁶¹Department of Radiology, University of Tokyo, Tokyo, Japan, ⁶²Systems Neuroscience and Pain Laboratory, Stanford University, Stanford, CA, United States, ⁶³Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, QC, Canada

Normative quantitative MRI values are useful for establishing diagnosis in individuals with a suspected disease. Building on the recent creation of an open-access multi-center database (n=248 subjects) of spinal cord MRI, we processed those data to extract quantitative metrics that are commonly used (cross-sectional area, diffusion and magnetization transfer metrics). Inter-vendor (Siemens, Philips, GE), inter- and intra-site coefficients of variation (COV) were calculated. Overall results suggest that the spinal cord generic acquisition protocol is reproducible across sites and vendors (COVs within 2-8%). The data and processing pipeline are publicly available at https://spine-generic.readthedocs.io/.