A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

Authors

Yudong Zhang, Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing
Jiquan Yang, Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing
Jianfei Yang, Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing
Aijun Liu, Arizona State University
Ping Sun, CUNY City College

Document Type

Article

Publication Date

2-2016

Abstract

Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use.

Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkagethresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) randomshift.

Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the leastmean absolute error, the leastmean-squared error, and the highest peak signal-to-noise ratio.

Conclusion. EWISTARS is superior to state-of-the-art approaches.

Comments

This article was originally published in the International Journal of Biomedical Imaging, available at http://dx.doi.org/10.1155/2016/9416435.

This is an open access article distributed under the Creative Commons Attribution License.