The application of plug-and-play ADMM framework and BM3D denoiser for compressed sensing MR image reconstruction

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is an effective technique to reduce MRI data acquisition time. There is currently growing interest in using Alternating Direction Method of Multiplier (ADMM) for CS-MRI reconstruction. In this paper, we propose a flexible plug-and-play framework to incorporate the block matching 3D (BM3D) denoising algorithm as prior into the plug-and-play ADMM reconstruction procedure for CS-MRI reconstruction, termed BM3D Plug-and-play ADMM (BPA) method. We investigated the performance of the proposed BPA method for the construction of highly under-sampled MRI data of two different sampling masks. Compared with other widely used CS-MRI reconstruction methods, such as, PANO, BM3D-IT, BM3D-MRI and BM3D-AMP-MRI, the proposed framework can reconstruct highly under-sampled CS-MRI data with improved gains in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).