2017/03/01 by Qingsong Yang, Wenxiang Cong, Ge Wang · 1 citation
Medicine · Engineering · #Medical Imaging Techniques and Applications #Advanced X-ray and CT Imaging #Radiomics and Machine Learning in Medical Imaging
paper · doi:10.1088/1361-6420/aa5e0a
openalex publication_date 2017/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.