The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Zhang, Minghan1; Gu, Sai1; Shi, Yuhui2

2022-05-01

10.1007/s40747-022-00724-7

Complex & Intelligent Systems   影响因子和分区

2199-4536

2198-6053

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.

Deep learning (DL) Artificial intelligence (AI) Low-dose computed tomography (LDCT) Systematic review

SCI

英语

通讯

Computer Science

Computer Science, Artificial Intelligence

WOS:000801120700002

SPRINGER HEIDELBERG

Web of Science

1.Univ Warwick, Sch Engn, Coventry, W Midlands, England
2.Southern Univ Sci & Technol, Sch Comp Sci & Engn, Shenzhen, Peoples R China

Zhang, Minghan,Gu, Sai,Shi, Yuhui. The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review[J]. Complex & Intelligent Systems,2022.

Zhang, Minghan,Gu, Sai,&Shi, Yuhui.(2022).The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review.Complex & Intelligent Systems.

Zhang, Minghan,et al."The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review".Complex & Intelligent Systems (2022).