Keywords: Diffusion Reconstruction, AI/ML Image Reconstruction
Motivation: High angular resolution diffusion MRI acquisitions are often clinically infeasible due to prolonged imaging times and patient discomfort. Current methods struggle to reconstruct high-dimensional diffusion data effectively from undersampled data with arbitrary sampling schemes.
Goal(s): Our aim is to enable accurate reconstruction of high-resolution diffusion signals from undersampled dMRI data, adaptable to variable sampling schemes without retraining.
Approach: We propose the Masked Diffusion Transformer (MDiT), integrating a random masking strategy for zero-shot generalization across sampling patterns and a DiT backbone with autoregressive inference for high-fidelity reconstruction.
Results: Our model achieved better results in the angular super resolution task compared to current state-of-the-art methods.
Impact: MDiT’s ability to accurately reconstruct diffusion data under various sampling schemes without retraining could advance clinical dMRI applications, supporting efficient, high-quality dMRI imaging even under practical clinical constraints.
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