Year: 2016
Author: Zhifeng Wu, Si Li, Xueying Zeng, Yuesheng Xu, A. Krol
Journal of Computational Mathematics, Vol. 34 (2016), Iss. 6 : pp. 626–647
Abstract
The purpose of this paper is to investigate the ability of the infimal convolution regularization in curing the staircasing artifacts of the TV model in the SPECT reconstruction. We formulate the problem of SPECT reconstruction with the infimal convolution regularization as a convex three-block optimization problem and characterize its solution by a system of fixed-point equations in terms of the proximity operator of the functions involved in its objective function. We then develop a novel fixed-point proximity algorithm based on the fixed-point equations. Moreover, we introduce a preconditioning matrix motivated by the classical MLEM (maximum-likelihood expectation maximization) algorithm. We prove convergence of the proposed algorithm. The numerical results are included to show that the infimal convolution regularization is capable of effectively reducing the staircasing artifacts, while maintaining comparable image quality in terms of the signal-to-noise ratio and coefficient recovery contrast.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1607-m2016-0537
Journal of Computational Mathematics, Vol. 34 (2016), Iss. 6 : pp. 626–647
Published online: 2016-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 22
Keywords: SPECT Infimal Convolution Regularization Staircasing Artifacts Fixed-point Proximity Algorithm.
Author Details
-
A Total Fractional-Order Variation Regularized Reconstruction Method for CT
Chen, Yun | Guo, Baoyu | Lu, YaoMathematical Problems in Engineering, Vol. 2020 (2020), Iss. P.1
https://doi.org/10.1155/2020/3714245 [Citations: 0] -
Infimal Convolution Regularized SPECT Image Reconstruction Based on Shearlet Transform
詹, 浩彬
Computer Science and Application, Vol. 12 (2022), Iss. 02 P.385
https://doi.org/10.12677/CSA.2022.122039 [Citations: 0] -
Sparsity promoting regularization for effective noise suppression in SPECT image reconstruction
Zheng, Wei | Li, Si | Krol, Andrzej | Ross Schmidtlein, C | Zeng, Xueying | Xu, YueshengInverse Problems, Vol. 35 (2019), Iss. 11 P.115011
https://doi.org/10.1088/1361-6420/ab23da [Citations: 10] -
A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm
Chen, Yun | Lu, Yao | Ma, Xiangyuan | Xu, YueshengInverse Problems, Vol. 38 (2022), Iss. 3 P.035005
https://doi.org/10.1088/1361-6420/ac490f [Citations: 1] -
X-ray image denoising for Cargo Dual Energy Inspection System
Yagoub, Bilel | Ibrahem, Hatem | Salem, Ahmed | Suh, Jae-Won | Kang, Hyun Soo2021 International Conference on Electronics, Information, and Communication (ICEIC), (2021), P.1
https://doi.org/10.1109/ICEIC51217.2021.9369786 [Citations: 6] -
A Krasnoselskii-Mann Algorithm With an Improved EM Preconditioner for PET Image Reconstruction
Lin, Yizun | Schmidtlein, C. Ross | Li, Qia | Li, Si | Xu, YueshengIEEE Transactions on Medical Imaging, Vol. 38 (2019), Iss. 9 P.2114
https://doi.org/10.1109/TMI.2019.2898271 [Citations: 6]