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

Authors: Xiaojun Yuan; Mingfeng Jiang; Lingyan Zhu; Yang Li; Yongming Li; Pin Wang; Tie-Qiang Li

Addresses: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China ' School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China ' The Dongfang College, Zhejiang University of Finance and Economics, Haining, China ' School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China ' College of Communication Engineering, Chongqing University, Chongqing, China ' College of Communication Engineering, Chongqing University, Chongqing, China ' Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden

Abstract: 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).

Keywords: MR image reconstruction; plug-and-play ADMM; denoising algorithm; compressed sensing.

DOI: 10.1504/IJCAT.2021.117268

International Journal of Computer Applications in Technology, 2021 Vol.65 No.4, pp.304 - 315

Received: 17 May 2020
Accepted: 10 Jul 2020
Published online: 31 Aug 2021 *

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