Distribution matching with subset‐ <i>k</i> ‐space embedding for multi‐contrast MRI reconstruction

The proposed DMSE model for MC-MRI reconstruction integrates a subset-k-space distribution prior and a high-dimensional global prior to guide the reconstruction process. By leveraging supplementary information from guidance contrasts and constrained information from the under-sampled data itself, DMSE significantly reduces noise and aliasing artifacts. Comparative and ablation experiments demonstrate that this method outperforms existing approaches in both quantitative and qualitative evaluations, achieving comparable reconstruction fidelity across different sampling conditions.