Total Variation Regularization CT Iterative Reconstruction Algorithm Based on Augmented Lagrangian Method

被引：0

作者：

Xiao D.-Y. ^{[1
]}

Guo Y. ^{[1
]}

Li J.-H. ^{[1
]}

Kang Y. ^{[1
]}

机构：

[1] School of Sino-Dutch Biomedical & Information Engineering, Northeastern University, Shenyang

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2018年 / 39卷 / 07期

关键词：

Augmented Lagrangian method; CT iterative reconstruction; Real projection data; Simulation data; Total variation regularization;

D O I：

10.12068/j.issn.1005-3026.2018.07.011

中图分类号：

学科分类号：

摘要：

A novel algorithm based on augmented Lagrangian method was presented to solve total variation regularization problem (ALMTVR) of the CT iterative reconstruction. The classical algebraic reconstruction technique (ART) was compared with the ALMTVR algorithm, the simulation data and actual data are used in the experiment. The ALMTVR algorithm and the ART algorithm were used to reconstruct the images respectively, and the reconstruction images were compared and analyzed. Results showed that, compared with ART algorithm, the proposed algorithm can significantly improve image quality and reconstruction speed, which indicates the proposed algorithm is effective and has potential applications in the CT imaging system. © 2018, Editorial Department of Journal of Northeastern University. All right reserved.

引用

页码：964 / 969

页数：5

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