A Novel Iterative Shrinkage Algorithm for CS-MRI via Adaptive Regularization

被引：31

作者：

Chen, Zhen ^{[1
]}

Fu, Yuli ^{[1
]}

Xiang, Youjun ^{[1
]}

Rong, Rong ^{[1
]}

机构：

[1] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China

来源：

IEEE SIGNAL PROCESSING LETTERS | 2017年 / 24卷 / 10期

关键词：

Compressed sensing (CS); image reconstruction; iterative shrinkage; magnetic resonance imaging (MRI); quasi-Newton method; RECONSTRUCTION;

D O I：

10.1109/LSP.2017.2736159

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Anew algorithm is proposed for compressed sensing-magnetic resonance imaging (CS-MRI). The l(p)-norm (0 < p <= 1) based adaptive regularization model is used for MRI. The algorithm is established by using a novel iterative shrinkage scheme. In the iteration, the quasi-Newton method is employed. In the shrinkage, the threshold is defined varyingly. Also, the parameter p is selected dynamically in the algorithm. Comparing with some certain state-of-the-art methods for the noisy case, the proposed algorithm provides a higher accuracy of the MR image reconstruction. The performance of the proposed algorithm is validated by the theoretical analysis as well as some experimental results.

引用

页码：1443 / 1447

页数：5

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