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Confocal histology provides an opportunity to establish intra-voxel fiberorientation distributions that can be used to quantitatively assess thebiological relevance of diffusion weighted MRI models, e.g., constrainedspherical deconvolution (CSD). Here, we apply deep learning to investigate thepotential of single shell diffusion weighted MRI to explain histologicallyobserved fiber orientation distributions (FOD) and compare the derived deeplearning model with a leading CSD approach. This study (1) demonstrates thatthere exists additional information in the diffusion signal that is notcurrently exploited by CSD, and (2) provides an illustrative data-driven modelthat makes use of this information.

Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution