论文｜Tao Wang, Wenjun Xia, Jingfeng Lu, and Yi Zhang*：A review of deep learning CT reconstruction from incomplete projection data

本文（A review of deep learning CT reconstruction from incomplete projection data）原载IEEE Transactions on Radiation and Plasma Medical Sciences，由四川大学张意教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract:Computed tomography (CT) is a widely used imaging technique in both medical and industrial applications. However, accurate CT reconstruction requires complete projection data, while incomplete data can result in significant artifacts in the reconstructed images, compromising their reliability for subsequent detection and diagnosis. As a result, accurate CT reconstruction from incomplete projection data remains a challenging research area in radiology. With the rapid development of deep learning (DL) techniques, many DL-based methods have been proposed for CT reconstruction from incomplete projection data. However, there are limited comprehensive surveys that summarize recent advances in this field. This paper provides a comprehensive overview of the current state-of-the-art DL-based CT reconstruction from incomplete projection data, including sparse-view reconstruction, limited-angle reconstruction, metal artifact reduction, interior tomography, and ring artifact reduction. This survey covers various DL-based solutions to the five problems, potential limitations of existing methods, and future research directions.

Tao Wang, Wenjun Xia, Jingfeng Lu, and Yi Zhang*. A review of deep learning CT reconstruction from incomplete projection data. IEEE Transactions on Radiation and Plasma Medical Sciences, pp. 138-152, vol. 8, no. 2, 2024.（论文下载）