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Abstract #5378

q-Space Novelty Detection in Short Diffusion MRI Scans of Multiple Sclerosis

Vladimir Golkov1, Aleksei Vasilev1, Francesco Pasa1,2, Ilona Lipp3, Wassim Boubaker1, Eleonora Sgarlata3,4, Franz Pfeiffer2, Valentina Tomassini3,5, Derek K. Jones3, and Daniel Cremers1

1Department of Informatics, Technical University of Munich, Munich, Germany, 2Physics Department, Technical University of Munich, Munich, Germany, 3CUBRIC, Cardiff University, Cardiff, United Kingdom, 4Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy, 5Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom

Diffusion MRI can capture disease-related microstructural changes, but most methods use handcrafted data transformations that discard parts of the information and require quite long scan times. In contrast, q-space novelty detection (q-ND) circumvents these drawbacks, and does not require any knowledge whatsoever about the effect of disease on q-space measurements. Instead, q-ND highlights voxels that look unlike anything seen in a database of healthy scans. Here we show that novelty scores from q-ND largely coincide with multiple sclerosis lesions, and that q-ND also works at reduced scan times.

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