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The use of deep learning methods in low-dose computed tomography image reconstruction : a systematic review
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Zhang, Minghan, Gu, Sai and Shi, Yuhui (2022) The use of deep learning methods in low-dose computed tomography image reconstruction : a systematic review. Complex & Intelligent Systems, 8 . pp. 5545-5561. doi:10.1007/s40747-022-00724-7
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WRAP-The-use-of-deep-learning-methods-in-low-dose-computed-tomography-image-reconstruction-a-systematic-review-Zhang-22.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (643Kb) | Preview |
Official URL: http://doi.org/10.1007/s40747-022-00724-7
Abstract
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.
Item Type: | Journal Item | ||||||||
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Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Deep learning (Machine learning), Artificial intelligence, Tomography | ||||||||
Journal or Publication Title: | Complex & Intelligent Systems | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 2199-4536 | ||||||||
Official Date: | December 2022 | ||||||||
Dates: |
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Volume: | 8 | ||||||||
Number of Pages: | 17 | ||||||||
Page Range: | pp. 5545-5561 | ||||||||
DOI: | 10.1007/s40747-022-00724-7 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 19 July 2022 | ||||||||
Date of first compliant Open Access: | 20 July 2022 |
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