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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