Distribution Matching with Subset-K-Space Embedding For Multi-Contrast MRI Reconstruction

Abstract

To reduce the time required for multiple acquisitions in multi-contrast magnetic resonance imaging (MC-MRI), recent research has focused on collecting partial k-space data from a single contrast to reconstruct high-quality images by leveraging the redundancy among different contrasts. Further exploiting relevant information across diverse contrasts presents a more effective solution for accurate reconstruction. This work proposes a novel reconstruction method that integrates the advantages of subset-k-space distribution prior and high-dimensional global prior for MC-MRI reconstruction. Specifically, the first stage involves the individual decomposition of k-space data from different guided contrasts, which are then combined with the measurements to construct a new subset-k-space. Notably, establishing this subset-k-space minimizes the distance between the distribution of the measurements and the target examples. In addition to capitalizing on the novel distribution matching strategy for improved sampling, the second stage incorporates global prior embedding to constrain the diffusion model within the high-dimensional space, using the reconstructed contrast itself as a reference. This global prior further refines the initial reconstruction obtained in the first stage. Empirical evaluations across various datasets compellingly demonstrate the proposed method's excellent capability to preserve details and achieve accurate reconstruction.

Keywords: Multi-contrast MRI reconstruction, diffusion model, subset-k-space embedding.