Abstract #0606

Identification of diffusion-based micro-structural measures most sensitive to multiple sclerosis focal damage using GAMER-MRI

Po-Jui Lu^1,2,3, Muhamed Barakovic^1,2,3, Matthias Weigel^1,2,3,4, Reza Rahmanzadeh^1,2,3, Riccardo Galbusera^1,2,3, Simona Schiavi⁵, Alessandro Daducci⁵, Francesco La Rosa^6,7,8, Meritxell Bach Cuadra^6,7,8, Robin Sandkühler⁹, Jens Kuhle^2,3, Ludwig Kappos^2,3, Philippe Cattin⁹, and Cristina Granziera^1,2,3

¹Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ²Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ³Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland, ⁴Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, ⁵Department of Computer Science, University of Verona, Verona, Italy, ⁶Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁷Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland, ⁸Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁹Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland

We applied an attention-based convolutional neural network to select discriminating diffusion measures derived from mathematical models of multi-shell diffusion data in the classification of multiple sclerosis lesions. Further, we correlated the selected measures or their combinations with the Expanded Disability Status Scale (EDSS) and the serum level of neurofilament light chain (sNfL). Our results show that the combinations have stronger correlations with EDSS and sNfL than the individual measures. The proposed method might be useful for selecting the microstructural measures most discriminative of focal tissue damage and identifying the combination most related to clinical disability and neuroaxonal damage.

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