Introduction
Bimonthly, started in 1957
Administrator
Shanxi Provincial Education Department
Sponsor
Taiyuan University of Technology
Publisher
Ed. Office of Journal of TYUT
Editor-in-Chief
SUN Hongbin
ISSN: 1007-9432
CN: 14-1220/N
Administrator
Shanxi Provincial Education Department
Sponsor
Taiyuan University of Technology
Publisher
Ed. Office of Journal of TYUT
Editor-in-Chief
SUN Hongbin
ISSN: 1007-9432
CN: 14-1220/N
location: home > paper >
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Research and Progress of Artificial Intelligence in Medical CT Image Reconstruction
DOI:
10.16355/j.cnki.issn1007-9432tyut.2023.01.001
Received:
Accepted:
| Corresponding author | Institute | |
| QIANG Yan | College of Information and Computer, Taiyuan University of Technology |
abstract:
Computed tomography (CT) is a medical image widely used in clinical medicine that visualizes the fine structural details inside human body. In clinical practice, to prevent the tissue damage caused by patient exposure to high-radiation X-ray beams, X-rays are usually minimized to obtain CT images, which results in a severe deterioration in imaging quality. To solve the above contradictions, how to reconstruct CT images that meet clinical needs has become a challenging problem widely concerned by researchers at home and abroad. With the vigorous development of deep learning technology in the field of artificial intelligence, the use of deep learning technology to improve the quality of CT reconstruction has become a hot topic in the current research under the drive of big data. In this paper, the mechanism of CT image reconstruction was analyzed. According to the imaging process of deep learning methods, the existing methods were divided into 4 categories, the basic ideas of 4 types of method introduced in turn, and the advantages and disadvantages of reconstruction methods summarized. The currently published public data set and the method of increasing training samples were summarized, and the diversity of loss functions; The problems that still exist in this emerging field are discussed, and the key problems that need to be solved in the follow-up research are looked forward so that relevant researchers can understand the research status in the field of CT reconstruction and promote the rapid development of the field.
Keywords:
artificial intelligence; computed tomography; image reconstruction; deep learning; projection domain; image domain; dual domain network