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Abstract #1264

A robust deep learning method for quantitative susceptibility mapping using diffusion model with a time-travel and resampling refinement module

Ming Zhang1 and Hongjiang Wei1,2
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, diffusion models

Motivation: Deep learning-based QSM reconstruction approaches often suffer from generalization problems.

Goal(s): To develop a robust deep learning-based method for QSM reconstruction using diffusion models along with a time-travel and resampling refinement strategy.

Approach: The diffusion prior is unconditionally trained using high-quality QSM images without explicit knowledge about the measurement. The physical constraint is plugged into the sampling process of the diffusion model by solving an inverse problem. A refinement strategy is proposed to apply the time-reverse and resampling strategy in the latter sampling steps.

Results: The proposed method shows high-quality and robust QSM reconstruction results compared with supervised deep learning-based methods.

Impact: We introduce a diffusion model-based method for QSM reconstruction by enforcing hard data consistency during inference. We also present a time-travel and resampling refinement module in the latter steps to enhance performance. Our approach enables robust and high-quality QSM reconstruction.

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Keywords