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Abstract #1132

Fully-integrated framework for registration of spinal cord white and gray matter

Sara Dupont1, Benjamin De Leener1, Manuel Taso2,3, Nikola Stikov1,4, Virginie Callot2,3, and Julien Cohen-Adad1,5

1Neuroimaging Research Laboratory (NeuroPoly), Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, QC, Canada, 2Centre de Résonance Magnétique Biologique et Médicale (CRMBM), UMR 7339, CNRS, Aix-Marseille Université, Marseille, France, 3Centre d'Exploration Métabolique par Résonance Magnétique (CEMEREM), Hôpital de la Timone, AP-HM, Marseille, France, 4Montreal Heart Institute (MHI), Montréal, QC, Canada, 5Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada

The spinal cord (SC) white and gray matter can be affected by a large number of pathologies. Being able to segment precisely the SC internal structure would be useful to better understand SC diseases, improve diagnosis and assess treatment efficiency. We introduce a complete framework for (i) multi-atlas automatic segmentation of the gray-matter, (ii) accurate registration to the MNI-Poly-AMU template and (iii) extraction of quantitative metric using partial volume information. Results showed improved accuracy of template registration when adding prior automatic gray-matter segmentation. The proposed method is freely available and provides an unbiased framework for quantitative analysis of SC MRI data.

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