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

11/13/2019 ∙ by Vishwesh Nath, et al. ∙ 0 ∙ share

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

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10/11/2019 ∙ by Fenghua Guo, et al. ∙ 0 ∙ share

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

Authors

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

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

DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging

HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI

Rotation-Equivariant Deep Learning for Diffusion MRI

Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution

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

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