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Artificial Intelligence-Based Image Reconstruction for Computed Tomography: A Survey

Quan Yan1, Yunfan Ye1, Jing Xia1, Zhiping Cai1,*, Zhilin Wang2, Qiang Ni3

1 College of Computer, National University of Defense Technology, Changsha, 410073, China
2 Technical Service Center for Vocational Education, National University of Defense Technology, Changsha, 410073, China
3 School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, UK

* Corresponding Author: Zhiping Cai. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 2545-2558.


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.


Cite This Article

Q. Yan, Y. Ye, J. Xia, Z. Cai, Z. Wang et al., "Artificial intelligence-based image reconstruction for computed tomography: a survey," Intelligent Automation & Soft Computing, vol. 36, no.3, pp. 2545–2558, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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