A modified POCS-based reconstruction method for compressively sampled MR imaging
One of the challenging tasks in the application of compressed sensing to magnetic resonance imaging is the reconstruction algorithm that can faithfully recover the MR image from randomly undersampled k-space data. The nonlinear recovery algorithms based on iterative shrinkage start with a single initial guess and use soft-thresholding to recover the original MR image from the partial Fourier data. This article presents a novel method based on projection onto convex set (POCS) algorithm but it takes two images and then randomly combines them at each iteration to estimate the original MR image. The performance of the proposed method is validated using the original data taken from the MRI scanner at St. Mary's Hospital, London. The experimental results show that the proposed method can reconstruct the original MR image from variable density undersampling scheme in less number of iterations and exhibits better performance in terms of improved signal-to-noise ratio, artifact power, and correlation as compared to the reconstruction through low-resolution and POCS algorithms.  2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 203207, 2014
compressed sensing iterated shrinkage sparse signal recovery rapid mri nonlinear reconstruction