An Efficient Augmented Lagrangian Method for Statistical X-Ray CT Image Reconstruction.
Statistical iterative reconstruction (SIR) for X-ray computed tomography (CT) under the penalized weighted least-squares criteria can yield significant gains over conventional analytical reconstruction from the noisy measurement. However, due to the nonlinear expression of the objective function, most exiting algorithms related to the SIR unavoidably suffer from heavy computation load and slow convergence rate, especially when an edge-preserving or sparsity-based penalty or regularization is incorporated. In this work, to address abovementioned issues of the general algorithms related to the SIR, we propose an adaptive nonmonotone alternating direction algorithm in the framework of augmented Lagrangian multiplier method, which is termed as "ALM-ANAD". The algorithm effectively combines an alternating direction technique with an adaptive nonmonotone line search to minimize the augmented Lagrangian function at each iteration. To evaluate the present ALM-ANAD algorithm, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present ALM-ANAD algorithm can achieve noticeable gains over the classical nonlinear conjugate gradient algorithm and state-of-the-art split Bregman algorithm in terms of noise reduction, contrast-to-noise ratio, convergence rate, and universal quality index metrics.
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Jiaojiao
J
Li
School of Mathematics and Computer Sciences, Gannan Normal University, Ganzhou 341000, China.
Shanzhou
S
Niu
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Jing
J
Huang
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Zhaoying
Z
Bian
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Qianjin
Q
Feng
School of Mathematics and Computer Sciences, Gannan Normal University, Ganzhou 341000, China.
Gaohang
G
Yu
Department of Radiology, State University of New York, Stony Brook, NY 11794, United States of America.
Zhengrong
Z
Liang
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Wufan
W
Chen
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Jianhua
J
Ma
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*