MRI reconstruction using a joint constraint in patch-based total variational framework

Compressed sensing (CS) as an efficient means has been widely applied in magnetic resonance imaging (MRI). As a regularization term to enforce the sparsity in the finite difference domain, the conventional total variation (TV) has been introduced in this field, where the staircase effect is presented. To overcome this issue, a new framework in the difference domain called joint constraint patch-based total variation (JCTV) is proposed. First, the image patch is utilized as the unit for TV norm to improve the adaptativity. Second, JCTV introduces a new nonlocal constraint term that exploits the estimated coefficients of the fully sampled image via linear minimum mean square error (LMMSE) criterion to improve the reconstruction performance. Finally, an alternative minimization algorithm is developed to seek the solution. Extensive experiments on a set of in vivo MR images demonstrate that the proposed algorithm outperforms the state-of-the-art approaches in terms of peak signal-to-noise ratio and visual quality.