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Abstract #3396

ReMiDi: Reconstruction of Microstructure from Diffusion MRI Signal

Prathamesh Pradeep Khole1, Zahra Kais Petiwala1, Shri Prathaa Magesh2, Ehsan Mirafzali1, Utkarsh Gupta1, Jing-Rebecca Li3, Andrada Ianus4, and Razvan Marinescu1
1Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States, 2Electrical Engineering, Indian Institute of Technology Madras, Chennai, India, 3Equipe Idefix, Inria-Saclay, Palaiseau, France, 4School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Synopsis

Keywords: Analysis/Processing, Diffusion Reconstruction, dMRI, Microstructure Reconstruction, Differentiable Simulators, Auto-Encoders, Spectral Representation

Motivation: Current dMRI techniques are limited to estimating simple 3D diffusion tensors indicating whether diffusion is restricted, and provides minimal voxel level microstructural detail.

Goal(s): To estimate the tissue microstructure at voxel level from dMRI signal.

Approach: We implement a differentiable dMRI simulator based on matrix formalism solution of BTPDE and use backpropagation guided by a decoder to reconstruct microstructure for a given signal.

Results: We successfully optimized arbitrary 3D meshes using a differentiable dMRI simulator combined with a spectral autoencoder. We also performed comprehensive ablation studies on 3D mesh shapes, and diffusion sequence parameters and directions.

Impact: Our work achieves a next-generation dMRI reconstruction, opening the ability to reconstruct brain microstructure with arbitrary meshes. This will enable in-vivo mesoscopic mapping of the human brain, and lead to improved biomarkers for Multiple Sclerosis and Traumatic Brain Injury.

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Keywords