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

11/13/2019

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

Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI

Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecture

Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE

DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging

E(3) × SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI

Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data

HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI

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

Related Research

Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI

Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecture

Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE

DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging

E(3) × SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI

Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data

HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI