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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 9576950, 11 pages
https://doi.org/10.1155/2017/9576950
Research Article

Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low-Rank Model

1School of Mathematics and Computing Science and Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
2Guangxi Experiment Center of Information Science, Guilin, Guangxi 541004, China
3School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China

Correspondence should be addressed to Xuewen Wang

Received 13 July 2017; Revised 12 October 2017; Accepted 5 November 2017; Published 19 December 2017

Academic Editor: Francesco Soldovieri

Copyright © 2017 Lixia Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The quality of dynamic magnetic resonance imaging reconstruction has heavy impact on clinical diagnosis. In this paper, we propose a new reconstructive algorithm based on the model. In the algorithm, the norm is substituted by the norm to approximate the norm; thus the accuracy of the solution is improved. We apply an alternate iteration method to solve the resulting problem of the proposed method. Experiments on nine data sets show that the proposed algorithm can effectively reconstruct dynamic magnetic resonance images.