TY - EJOU AU - Yan, Quan AU - Ye, Yunfan AU - Xia, Jing AU - Cai, Zhiping AU - Wang, Zhilin AU - Ni, Qiang TI - Artificial Intelligence-Based Image Reconstruction for Computed Tomography: A Survey T2 - Intelligent Automation \& Soft Computing PY - 2023 VL - 36 IS - 3 SN - 2326-005X AB - Computed tomography has made significant advances since its introduction in the early 1970s, where researchers have mainly focused on the quality of image reconstruction in the early stage. However, radiation exposure poses a health risk, prompting the demand of the lowest possible dose when carrying out CT examinations. To acquire high-quality reconstruction images with low dose radiation, CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction, to reconstruction methods based on artificial intelligence (AI). All these efforts are devoted to constructing high-quality images using only low doses with fast reconstruction speed. In particular, conventional reconstruction methods usually optimize one aspect, while AI-based reconstruction has finally managed to attain all goals in one shot. However, there are limitations such as the requirements on large datasets, unstable performance, and weak generalizability in AI-based reconstruction methods. This work presents the review and discussion on the classification, the commercial use, the advantages, and the limitations of AI-based image reconstruction methods in CT. KW - Computed tomography; image reconstruction; artificial intelligence DO - 10.32604/iasc.2023.029857