MS lesions have heterogeneous pathology, including inflammation, demyelination, axonal injury, and neuronal loss. Our laboratory has developed a diffusion basis spectrum imaging (DBSI) technique to address the shortcomings of MRI-based MS. Primary DBSI metrics have been demonstrated to be associated with MS pathologies in animal models and human tissue. We propose that profiles of multiple DBSI metrics can identify important patterns within MS lesions and normal appearing white matter. Here we report that Diffusion Histology Imaging (DHI), an improved approach that combines a deep neural network (DNN) algorithm with DBSI-derived diffusion metrics, accurately detected and classified various MS lesion subtypes.
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