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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.04638 |
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| _version_ | 1866911486115315712 |
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| author | Khole, Prathamesh Pradeep Brenes, Mario M. Petiwala, Zahra Kais Mirafzali, Ehsan Gupta, Utkarsh Li, Jing-Rebecca Ianus, Andrada Marinescu, Razvan |
| author_facet | Khole, Prathamesh Pradeep Brenes, Mario M. Petiwala, Zahra Kais Mirafzali, Ehsan Gupta, Utkarsh Li, Jing-Rebecca Ianus, Andrada Marinescu, Razvan |
| contents | Diffusion MRI (dMRI) is sensitive to microstructural barriers, yet most existing methods either assume impermeable boundaries or estimate voxel-level parameters without recovering explicit interfaces. We present Spinverse, a permeability-aware reconstruction method that inverts dMRI measurements through a fully differentiable Bloch-Torrey simulator. Spinverse represents tissue on a fixed tetrahedral grid and treats each interior face permeability as a learnable parameter; low-permeability faces act as diffusion barriers, so microstructural boundaries whose topology is not fixed a priori (up to the resolution of the ambient mesh) emerge without changing mesh connectivity or vertex positions. Given a target signal, we optimize face permeabilities by backpropagating a signal-matching loss through the PDE forward model, and recover an interface by thresholding the learned permeability field. To mitigate the ill-posedness of permeability inversion, we use mesh-based geometric priors; to avoid local minima, we use a staged multi-sequence optimization curriculum. Across a collection of synthetic voxel meshes, Spinverse reconstructs diverse geometries and demonstrates that sequence scheduling and regularization are critical to avoid outline-only solutions while improving both boundary accuracy and structural validity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04638 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spinverse: Differentiable Physics for Permeability-Aware Microstructure Reconstruction from Diffusion MRI Khole, Prathamesh Pradeep Brenes, Mario M. Petiwala, Zahra Kais Mirafzali, Ehsan Gupta, Utkarsh Li, Jing-Rebecca Ianus, Andrada Marinescu, Razvan Computer Vision and Pattern Recognition Machine Learning Quantitative Methods Diffusion MRI (dMRI) is sensitive to microstructural barriers, yet most existing methods either assume impermeable boundaries or estimate voxel-level parameters without recovering explicit interfaces. We present Spinverse, a permeability-aware reconstruction method that inverts dMRI measurements through a fully differentiable Bloch-Torrey simulator. Spinverse represents tissue on a fixed tetrahedral grid and treats each interior face permeability as a learnable parameter; low-permeability faces act as diffusion barriers, so microstructural boundaries whose topology is not fixed a priori (up to the resolution of the ambient mesh) emerge without changing mesh connectivity or vertex positions. Given a target signal, we optimize face permeabilities by backpropagating a signal-matching loss through the PDE forward model, and recover an interface by thresholding the learned permeability field. To mitigate the ill-posedness of permeability inversion, we use mesh-based geometric priors; to avoid local minima, we use a staged multi-sequence optimization curriculum. Across a collection of synthetic voxel meshes, Spinverse reconstructs diverse geometries and demonstrates that sequence scheduling and regularization are critical to avoid outline-only solutions while improving both boundary accuracy and structural validity. |
| title | Spinverse: Differentiable Physics for Permeability-Aware Microstructure Reconstruction from Diffusion MRI |
| topic | Computer Vision and Pattern Recognition Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2603.04638 |