ArticleMedical image analysis2021
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- DeepFixel: Crossing white matter fiber identification through spherical convolutional neural networks.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- A review on learning-based algorithms for tractography and human brain white matter tracts recognition.Neuroradiology · 2025Review
- FODSeg: a deep learning framework for tract-specific white matter segmentation from full angular distributions.Frontiers in neuroscience · 2025Article
- White Matter Tract Crossing and Bottleneck Regions in the Fetal Brain.Human brain mapping · 2025Article
- White matter tract crossing and bottleneck regions in the fetal brain.bioRxiv : the preprint server for biology · 2024Article
- High-angular resolution diffusion imaging generation using 3d u-net.Neuroradiology · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Fast multi-compartment Microstructure Fingerprinting in brain white matter.Frontiers in neuroscience · 2024Article
- Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview.Frontiers in neurology · 2023Review
- Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review.Frontiers in neuroscience · 2023Review
- Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.NeuroImage · 2022Article
- Article
- Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data.Information processing in medical imaging : proceedings of the ... conference · 2021Article
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Authors and funding
7 authors.
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Abstract
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.
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