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Abstract #4819

Autoregressive Masked Diffusion Transformer for Consistent Zero-shot Angular Space Super-Resolution in Diffusion MRI

Mu Nan1, Wenxin Fan2, Juan Zou3, Ruoyou Wu2, and Shanshan Wang2
1Paul C. Lauterbur ResearchCenter for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 3School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha, China

Synopsis

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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