Abstract:

Multi-contrast MRI allows for the simultaneous acquisition of multiple weighted images, enhancing imaging efficiency and providing rich quantitative information. However, reconstructing these images under high undersampling while ensuring anatomical consistency and physical plausibility remains a significant challenge. Existing methods often rely on scarce fully-sampled data or suffer from performance degradation due to domain shift. To address this, we propose a physics-prior-guided diffusion model that encodes MR signal evolution via Bloch equations into a dictionary-matching constraint. This constraint is directly coupled into the reverse sampling process, enabling physical correction of data-driven priors without retraining. Experimental results demonstrate superior generalization over supervised and self-supervised approaches, while the estimated parameter maps validate its high quantitative accuracy, highlighting its potential for clinical multi-contrast imaging.

Key words: magnetic resonance imaging (MRI), Multi-contrast Imaging, Diffusion Models, Unsupervised Learning, Physics Priors