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Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI
Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only non-i...
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Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE
Abstract. Intra-voxel models of the diffusion signal are essential for i...
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DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging
DELIMIT is a framework extension for deep learning in diffusion imaging,...
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HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
In this work we propose HAMLET, a novel tract learning algorithm, which,...
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Rotation-Equivariant Deep Learning for Diffusion MRI
Convolutional networks are successful, but they have recently been outpe...
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Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
We propose two strategies to improve the quality of tractography results...
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Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data
Spherical deconvolution is a widely used approach to quantify fiber orie...
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Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution
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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