caa a22 4500 605546037 CHVBK 20210128100938.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00723-015-0683-2 doi (NATIONALLICENCE)springer-10.1007/s00723-015-0683-2 Compressively Sampled MR Image Reconstruction Using Hyperbolic Tangent-Based Soft-Thresholding [Elektronische Daten] [Jawad Shah, I. Qureshi, Julio Proano, Yiming Deng] The application of compressed sensing (CS) to magnetic resonance (MR) images utilizes the transformed domain sparsity to enable the reconstruction from an under-sampled k-space (Fourier) data using a non-linear recovery algorithm. In order to estimate the missing k-space data from the partial Fourier samples, the reconstruction algorithms minimize an objective function based on mixed l 1−l 2 norms. Iterative-shrinkage algorithms, such as parallel coordinate descent (PCD) and separable surrogate functional, provide an efficient numerical technique to minimize the l 1-regularized least square optimization problem. These algorithms require a thresholding step to induce sparsity in the solution, which is an essential requirement of the CS recovery. This paper introduces a novel soft-thresholding method based on the hyperbolic tangent function. It has been shown that by using the proposed thresholding function in the sparsifying domain and a data consistency step in the k-space, the iterative-shrinkage algorithms can be used effectively to recover the under-sampled MR images. For the purpose of demonstration, we use the proposed soft-thresholding and data consistency with the PCD algorithm and compare its performance with the conventional PCD, projection onto convex sets and low-resolution reconstruction methods. The metrics used to compare the various algorithms are the artifact power, the peak signal-to-noise ratio, the correlation and the structural similarity index. The experimental results are validated using Shepp-Logan phantom image as well as real human head MR images taken from the MRI scanner at St. Mary's Hospital, London. Springer-Verlag Wien, 2015 Shah Jawad Department of Electrical Engineering, University of Colorado Denver and Anschutz Medical Campus, Denver, USA aut Qureshi I. Department of Electrical Engineering, Institute of Signals, Systems and Soft computing (ISSS), Air University, Islamabad, Pakistan aut Proano Julio Department of Electrical Engineering, University of Colorado Denver and Anschutz Medical Campus, Denver, USA aut Deng Yiming Department of Electrical Engineering, University of Colorado Denver and Anschutz Medical Campus, Denver, USA aut Applied Magnetic Resonance Springer Vienna 46/8(2015-08-01), 837-851 0937-9347 46:8<837 2015 46 723 https://doi.org/10.1007/s00723-015-0683-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00723-015-0683-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Shah Jawad Department of Electrical Engineering, University of Colorado Denver and Anschutz Medical Campus, Denver, USA aut NATIONALLICENCE 700 1- Qureshi I. Department of Electrical Engineering, Institute of Signals, Systems and Soft computing (ISSS), Air University, Islamabad, Pakistan aut NATIONALLICENCE 700 1- Proano Julio Department of Electrical Engineering, University of Colorado Denver and Anschutz Medical Campus, Denver, USA aut NATIONALLICENCE 700 1- Deng Yiming Department of Electrical Engineering, University of Colorado Denver and Anschutz Medical Campus, Denver, USA aut NATIONALLICENCE 773 0- Applied Magnetic Resonance Springer Vienna 46/8(2015-08-01), 837-851 0937-9347 46:8<837 2015 46 723