Abstract #1169

Automatic Quantification Pipeline for Spinal Cord Grey and White Matter in Multiple Sclerosis

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.

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