A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction

Abstract

Multi-contrast magnetic resonance imaging (MRI) can provide richer diagnosis information. The data acquisition time, however, is increased than single-contrast imaging. To reduce this time, k-space undersampling is an effective way but a smart reconstruction algorithm is required to remove undersampling image artifacts. Traditional algorithms commonly explore the similarity of multi-contrast images through joint sparsity. However, these algorithms are time-consuming due to the iterative process and require adjusting hyperparameters manually. Recently, data-driven deep learning successfully overcome these limitations but the reconstruction error still needs to be further reduced. Here, we propose a Joint Group Sparsity-based Network (JGSN) for multi-contrast MRI reconstruction, which unrolls the iterative process of the joint sparsity algorithm. The designed network includes data consistency modules, learnable sparse transform modules, and joint group sparsity constraint modules. In particular, weights of different contrasts in the transform module are shared to reduce network parameters without sacrificing the quality of reconstruction. The experiments were performed on the retrospective undersampled brain and knee data. Experimental results on in vivo brain data and knee data show that our method consistently outperforms the state-of-the-art methods under different sampling patterns.