多对比度磁共振成像技术通过一次扫描同步获取多对比度加权图像，在提升成像效率的同时也为后续参数定量提供了丰富信息．然而，高度欠采样条件下同时重建多个对比度图像并保证其解剖一致性与物理合理性，仍是该领域面临的关键挑战．现有方法或依赖难以获取的全采样标签数据，或面临训练域与目标域分布不一致时性能下降的域偏移问题．为此，本文提出一种物理先验引导的多对比度重建扩散模型，该方法将布洛赫动力学方程所描述的磁共振信号演化规律编码为字典匹配约束，直接耦合至扩散模型的反向采样过程中，在无需重新训练的前提下，实现对数据驱动先验的物理校正．实验表明，本方法较现有监督及自监督学习方法泛化能力更强，估算的参数图进一步验证了其参数定量准确性，有望推动多对比度定量成像在临床中的广泛应用．

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.