Joint Calibrationless Reconstruction of Highly Undersampled Multi-Contrast MR Datasets Using A Novel Low-Rank Completion Approach

Abstract

Routine clinical MRI session often requires multi-contrast imaging with identical geometries but different contrasts, and these images of different contrasts are independently reconstructed despite ubiquitous similarities. Simultaneous autocalibrating and k-space estimation (SAKE) provides a powerful calibrationless parallel imaging approach to reduce scanning time through undersampling. However, traditional SAKE reconstruction does not utilize redundant information embedded in multi-contrast datasets. In this study, we propose to advance SAKE by jointly reconstructing concatenated multi-contrast datasets using a novel low-rank completion approach. Our new method explicitly exploits the correlations in multi-contrast datasets and outperforms the traditional SAKE, leading to higher acceleration factors.