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.
@article{arxiv.1911.07927,
title = {Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution},
author = {Vishwesh Nath and Kurt G. Schilling and Colin B. Hansen and Prasanna Parvathaneni and Allison E. Hainline and Camilo Bermudez and Andrew J. Plassard and Vaibhav Janve and Yurui Gao and Justin A. Blaber and Iwona Stępniewska and Adam W. Anderson and Bennett A. Landman},
journal= {arXiv preprint arXiv:1911.07927},
year = {2019}
}
Comments
2 pages, 4 figures. This work was accepted and published as an abstract at ISMRM 2018 held in Paris, France