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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Q. Yan
  • Y. Ye
  • J. Xia
  • Z. Cai
  • Z. Wang
  • Q. Ni
<mark>Journal publication date</mark>15/03/2023
<mark>Journal</mark>Intelligent Automation and Soft Computing
Issue number3
Number of pages14
Pages (from-to)2545-2558
Publication StatusPublished
<mark>Original language</mark>English


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 con-structing 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.