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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.01988 |
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| _version_ | 1866929735157678080 |
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| author | Khole, Prathamesh Pradeep Petiwala, Zahra Kais Magesh, Shri Prathaa Mirafzali, Ehsan Gupta, Utkarsh Li, Jing-Rebecca Ianus, Andrada Marinescu, Razvan |
| author_facet | Khole, Prathamesh Pradeep Petiwala, Zahra Kais Magesh, Shri Prathaa Mirafzali, Ehsan Gupta, Utkarsh Li, Jing-Rebecca Ianus, Andrada Marinescu, Razvan |
| contents | We propose ReMiDi, a novel method for inferring neuronal microstructure as arbitrary 3D meshes using a differentiable diffusion Magnetic Resonance Imaging (dMRI) simulator. We first implemented in PyTorch a differentiable dMRI simulator that simulates the forward diffusion process using a finite-element method on an input 3D microstructure mesh. To achieve significantly faster simulations, we solve the differential equation semi-analytically using a matrix formalism approach. Given a reference dMRI signal $S_{ref}$, we use the differentiable simulator to iteratively update the input mesh such that it matches $S_{ref}$ using gradient-based learning. Since directly optimizing the 3D coordinates of the vertices is challenging, particularly due to ill-posedness of the inverse problem, we instead optimize a lower-dimensional latent space representation of the mesh. The mesh is first encoded into spectral coefficients, which are further encoded into a latent $\textbf{z}$ using an auto-encoder, and are then decoded back into the true mesh. We present an end-to-end differentiable pipeline that simulates signals that can be tuned to match a reference signal by iteratively updating the latent representation $\textbf{z}$. We demonstrate the ability to reconstruct microstructures of arbitrary shapes represented by finite-element meshes, with a focus on axonal geometries found in the brain white matter, including bending, fanning and beading fibers. Our source code is available online. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01988 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReMiDi: Reconstruction of Microstructure Using a Differentiable Diffusion MRI Simulator Khole, Prathamesh Pradeep Petiwala, Zahra Kais Magesh, Shri Prathaa Mirafzali, Ehsan Gupta, Utkarsh Li, Jing-Rebecca Ianus, Andrada Marinescu, Razvan Image and Video Processing Graphics Machine Learning Medical Physics We propose ReMiDi, a novel method for inferring neuronal microstructure as arbitrary 3D meshes using a differentiable diffusion Magnetic Resonance Imaging (dMRI) simulator. We first implemented in PyTorch a differentiable dMRI simulator that simulates the forward diffusion process using a finite-element method on an input 3D microstructure mesh. To achieve significantly faster simulations, we solve the differential equation semi-analytically using a matrix formalism approach. Given a reference dMRI signal $S_{ref}$, we use the differentiable simulator to iteratively update the input mesh such that it matches $S_{ref}$ using gradient-based learning. Since directly optimizing the 3D coordinates of the vertices is challenging, particularly due to ill-posedness of the inverse problem, we instead optimize a lower-dimensional latent space representation of the mesh. The mesh is first encoded into spectral coefficients, which are further encoded into a latent $\textbf{z}$ using an auto-encoder, and are then decoded back into the true mesh. We present an end-to-end differentiable pipeline that simulates signals that can be tuned to match a reference signal by iteratively updating the latent representation $\textbf{z}$. We demonstrate the ability to reconstruct microstructures of arbitrary shapes represented by finite-element meshes, with a focus on axonal geometries found in the brain white matter, including bending, fanning and beading fibers. Our source code is available online. |
| title | ReMiDi: Reconstruction of Microstructure Using a Differentiable Diffusion MRI Simulator |
| topic | Image and Video Processing Graphics Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2502.01988 |