Yang Q, Cong W, Wang G
Biomedical Imaging Center, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, NY, United States of America.
Inverse Probl. 2017;33(4). doi: 10.1088/1361-6420/aa5e0a. Epub 2017 Mar 1.
The recently-developed superiorization approach is efficient and robust for solving various constrained optimization problems. This methodology can be applied to multi-energy CT image reconstruction with the regularization in terms of the prior rank, intensity and sparsity model (PRISM). In this paper, we propose a superiorized version of the simultaneous algebraic reconstruction technique (SART) based on the PRISM model. Then, we compare the proposed superiorized algorithm with the Split-Bregman algorithm in numerical experiments. The results show that both the Superiorized-SART and the Split-Bregman algorithms generate good results with weak noise and reduced artefacts.
最近开发的超优化方法在解决各种约束优化问题方面高效且稳健。这种方法可应用于基于先验秩、强度和稀疏性模型（PRISM）进行正则化的多能量CT图像重建。在本文中，我们提出了一种基于PRISM模型的同时代数重建技术（SART）的超优化版本。然后，我们在数值实验中将提出的超优化算法与分裂Bregman算法进行比较。结果表明，超优化SART算法和分裂Bregman算法在弱噪声和减少伪影方面都产生了良好的结果。
