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.

中文翻译：

---

基于优势的多能CT图像重建

最近开发的优化方法对于解决各种约束优化问题是有效且稳健的。该方法可以应用于具有先验秩、强度和稀疏模型（PRISM）方面的正则化的多能量 CT 图像重建。在本文中，我们提出了基于 PRISM 模型的同时代数重建技术 (SART) 的高级版本。然后，我们在数值实验中将所提出的优越算法与 Split-Bregman 算法进行了比较。结果表明，Superiorized-SART 和 Split-Bregman 算法都产生了良好的结果，具有弱噪声和减少的伪影。