ArticleInformation processing in medical imaging : proceedings of the ... conference2021
Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data.
Article in Information processing in medical imaging : proceedings of the ... conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep Learning for fODF Estimation in Infant Brains: Model Comparison, Ground-Truth Impact, and Domain Shift Mitigation.Human brain mapping · 2025Article
- A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.Medical image analysis · 2025Article
- Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of diffusion MRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data.Frontiers in neuroimaging · 2024Article
- E(3) × SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI.Proceedings of machine learning research · 2024Article
- Sparse Blind Spherical Deconvolution of diffusion weighted MRI.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a rotation-equivariant self-supervised learning framework for the sparse deconvolution of non-negative scalar fields on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tissue structure such as white matter. Due to spatial and spectral partial voluming, clinically-feasible dMRI struggles to resolve crossing-fiber white matter configurations, leading to extensive development in spherical deconvolution methodology to recover underlying fiber directions. However, these methods are typically linear and struggle with small crossing-angles and partial volume fraction estimation. In this work, we improve on current methodologies by nonlinearly estimating fiber structures via self-supervised spherical convolutional networks with guaranteed equivariance to spherical rotation. We perform validation via extensive single and multi-shell synthetic benchmarks demonstrating competitive performance against common base-lines. We further show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset. Finally, we show downstream improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.