Abstract #5225

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

Vishwesh Nath¹, Kurt G Schilling², Prasanna Parvathaneni³, Allison E Hainline⁴, Colin B Hansen¹, Camilo Bermudez², Andrew J Plassard¹, Justin A Blaber¹, Vaibhav Janve², Yurui Gao², Iwona Stepniewska⁵, Adam W Anderson², and Bennett A Landman³

¹Computer Science, Vanderbilt University, Nashville, TN, United States, ²Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ³Electrical Engineering, Vanderbilt University, Nashville, TN, United States, ⁴Biostatistics, Vanderbilt University, Nashville, TN, United States, ⁵Psychology, Vanderbilt University, Nashville, TN, United States

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.

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