[1]张明辉,肖凯,卢红阳,等.基于加权双层Bregman及图结构正则化的磁共振成像[J].深圳大学学报理工版,2016,33(2):119-126.[doi:10.3724/SP.J.1249.2016.02119]
　Zhang Minghui,Xiao Kai,Lu Hongyang,et al.Weighted two-level Bregman method with graph regularized sparse coding for MRI reconstruction[J].Journal of Shenzhen University Science and Engineering,2016,33(2):119-126.[doi:10.3724/SP.J.1249.2016.02119]
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基于加权双层Bregman及图结构正则化的磁共振成像()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第33卷
期数:
2016年第2期
页码:
119-126
栏目:
电子与信息科学
出版日期:
2016-03-20

文章信息/Info

Title:
Weighted two-level Bregman method with graph regularized sparse coding for MRI reconstruction
文章编号:
201602002
作者:
张明辉; 肖凯; 卢红阳; 徐晓玲
南昌大学信息工程学院，江西南昌330031
Author(s):
Zhang Minghui;  Xiao Kai;  Lu Hongyang;  and Xu Xiaoling
School of Information Engineering, Nanchang University, Nanchang 330031, Jiangxi Province, P.R.China
关键词:
图像处理; 磁共振成像; 压缩感知; 图结构正则化稀疏表示; 字典学习; 加权双层伯格曼迭代; 交替方向法
Keywords:
image processing;  magnetic resonance imaging;  compressed sensing;  graph regularized sparse coding;  dictionary learning;  weighted Bregman iterative method;  alternating direction method
分类号:
N 34
DOI:
10.3724/SP.J.1249.2016.02119
文献标志码:
A
摘要:
针对磁共振图像(magnetic resonance imaging, MRI)重建质量的问题，提出一种基于加权双层Bregman字典学习方法和图结构正则化稀疏表示的新算法．该算法中，迭代重加权最小l1和图结构正则化稀疏表示模型是被合并到双层Bregman字典学习方法中．加权双层Breman的字典学习方法在外层迭代中增强K空间抽样数据的约束性，在内层迭代中解决Lp的优化．而图结构正则化稀疏表示方法具备捕获图像结构细节的能力，所以从较高的欠采样数据中能完成精确重建．此外，在内层迭代中，重加权最小l1和图结构正则化稀疏表示使算法能快速地趋于收敛．实验结果表明，所提出的算法可有效恢复MRI图像，其峰值信噪比和高频错误的值都优于基于压缩感知的字典学习方法和基于双层Bregman的自适应字典学习方法．
Abstract:
To improve the quality of magnetic resonance imaging, we propose a new dictionary learning algorithm integrating the weighted two-level Bregman and graph regularized sparse coding. We incorporate the iteratively reweighted l1-minimization and graph regularized sparse coding model into the two-level Bregman method with dictionary updating (TBMDU). The weighted two-level Bregman iterative procedure enforces the constraints of K-space sampled data in the outer-level and solves Lp-optimization in the inner-level. The graph regularized sparse coding model has great capacity in capturing structural details of images and, consequently, enables accurate reconstruction from highly under-sampled data. Furthermore, the proposed algorithm is able to converge with a relatively small number of iterations due to the reweighted l1-minimization iteration and graph regularized sparse coding applied in the inner minimization. Simulation results demonstrate that the proposed algorithm can reconstruct MRI images efficiently and outperforms some current approaches, such as dictionary learning for compressed sensing and two-level Bregman method with dictionary updating, in terms of the peak signal-to-noise ratio and the norm value of high-frequency error.

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备注/Memo

备注/Memo:
Received：2015-12-10；Accepted：2016-02-16
Foundation：National Natural Science Foundation of China (61362001); Natural Science Foundation of Jiangxi Province (20151BAB207007)
Corresponding author：Professor Zhang Minghui. E-mail: zhangmh3529@163.com
Citation：Zhang Minghui，Xiao Kai，Lu Hongyang，et al. Weighted two-level bregman method with graph regularized sparse coding for MRI reconstruction［J］. Journal of Shenzhen University Science and Engineering, 2016, 33(2): 119-126.（in Chinese）
基金项目：国家自然科学基金资助项目（61362001）；江西省自然科学基金资助项目 (20151BAB207007)
作者简介：张明辉（1963—），男，南昌大学教授．研究方向：压缩感知，磁共振成像．E-mail: zhangmh3529@163.com
引文：张明辉，肖凯，卢红阳，等．基于加权双层Bregman及图结构正则化的磁共振成像［J］. 深圳大学学报理工版，2016，33（2）：119-126.
更新日期/Last Update: 2016-03-04