  Confocal histology provides an opportunity to establish intra-voxel fiber
orientation distributions that can be used to quantitatively assess the
biological relevance of diffusion weighted MRI models, e.g., constrained
spherical deconvolution (CSD). Here, we apply deep learning to investigate the
potential of single shell diffusion weighted MRI to explain histologically
observed fiber orientation distributions (FOD) and compare the derived deep
learning model with a leading CSD approach. This study (1) demonstrates that
there exists additional information in the diffusion signal that is not
currently exploited by CSD, and (2) provides an illustrative data-driven model
that makes use of this information.
