ArticleScientific reports2021
Probing tissue microstructure by diffusion skewness tensor imaging.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Beyond Directions: Symmetry-Aware Rotation Sets for Triaxial Diffusion Encoding by Geometric Filter Optimization.Magnetic resonance in medicine · 2026Article
- Geometry of the cumulant series in diffusion MRI.Nature communications · 2026Article
- Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.Medical image analysis · 2025Article
- In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI.Human brain mapping · 2024Article
- Article
- Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.NeuroImage · 2022Review
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.
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Registered trials
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