Efficient Compressed Sensing Reconstruction Using Group Sparse Total Variation Regularization

摘要

Compressed Sensing MR imaging (CS-MRI) is one of the most promising approaches to reconstruct the MR image from highly under-sampled k-space data, while greatly reducing scanning time. Total variation (TV) is commonly used as a penalty in sparse signal recovery. In this paper, an extension of TV, termed Group-Sparse Total Variation (GSTV), is employed as a regularization term for the CS-MR image reconstruction. Three different types of MR images with two different sampling trajectories are used to validate the performance of the proposed method in reconstruction accuracy and computational efficiency. Compared with the patch-based nonlocal Operator (PANO) method, the proposed GSTV-based regularization method achieves comparable SNR but owns the advantage of faster computation. Moreover, the proposed method can obviously improve the reconstruction quality with higher signal-to-noise ratio (SNR) than the Wavelet tree sparsity method (WaTMRI), however it takes some more reconstruction time. In all, the proposed method provides a simple and effective alternative solution to the CS-MRI regularization framework.