文章基本信息

标题：A Symmetric ADMM for Non-convex Regularization Magnetic Resonance Imaging
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作者：Zhijun Luo ; Zhibin Zhu ; Benxin Zhang 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：4
语种：English
出版社：IAENG - International Association of Engineers
摘要：The total variation (TV) regularization technique is a popular method for magnetic resonance imaging (MRI) reconstruction. In this paper, the generalized minimax concave (GMC) penalty function is used to construct a nonconvex regularized MRI model, which can effectively prevent the systematic underestimation characteristic of the standard TV regularization. In addition, the cost function can maintain convexity under certain conditions. To solve the new non-convex model, we describe a symmetric alternating direction method of multipliers (S-ADMM) algorithm, which is faster than the original ADMM. The experiment results show the effectiveness of the proposed model and algorithm.
关键词：MRI reconstruction;ADMM;TV regularization;minimax-concave penalty