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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