Open Access

ARTICLE

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

by Quan Yan¹, Yunfan Ye¹, Jing Xia¹, Zhiping Cai^1,*, 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:

Intelligent Automation & Soft Computing 2023, 36(3), 2545-2558. https://doi.org/10.32604/iasc.2023.029857

Received 13 March 2022; Accepted 19 April 2022; Issue published 15 March 2023

Abstract

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.

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Keywords

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