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Abstract #0606

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

Po-Jui Lu1,2,3, Muhamed Barakovic1,2,3, Matthias Weigel1,2,3,4, Reza Rahmanzadeh1,2,3, Riccardo Galbusera1,2,3, Simona Schiavi5, Alessandro Daducci5, Francesco La Rosa6,7,8, Meritxell Bach Cuadra6,7,8, Robin Sandkühler9, Jens Kuhle2,3, Ludwig Kappos2,3, Philippe Cattin9, and Cristina Granziera1,2,3
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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Abstract