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arXiv · 2609.28958

Revolutionizing Diffusion MRI Microstructure Mapping via Global Inversion

Yuxiang Wan·Hamza Farooq·Wenjie Zhang·Qiaozhi Huang·Lingjie Su·Christophe Lenglet·Ju Sun
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Abstract

Diffusion MRI microstructure mapping (MM) is conventionally solved voxel by voxel, ignoring the fact that tissue microstructure forms a spatially organized field. This isolation leaves each estimation problem ill-posed and nonconvex. We instead cast MM as a single global inverse problem, reconstructing the entire parameter field jointly from all measurements of a subject. An untrained neural representation supplies implicit spatial priors and eases the nonconvex optimization, requiring no training data, while coregistered T1-weighted anatomy contributes structural guidance that is freely available in standard protocols. On both synthetic and in-vivo data, our method compares favorably with established voxel-wise and learning-based baselines, suggesting global inversion is a promising alternative.

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Yuxiang Wan, Hamza Farooq, Wenjie Zhang, Qiaozhi Huang, Lingjie Su, Christophe Lenglet, Ju Sun. 2026-09-24. Revolutionizing Diffusion MRI Microstructure Mapping via Global Inversion. https://arxiv.org/abs/2609.28958

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