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Artif Intell Med Imaging. Aug 28, 2021; 2(4): 86-94
Published online Aug 28, 2021. doi: 10.35711/aimi.v2.i4.86
Current status of deep learning in abdominal image reconstruction
Guang-Yuan Li, Cheng-Yan Wang, Jun Lv
Guang-Yuan Li, Jun Lv, School of Computer and Control Engineering, Yantai University, Yantai 264000, Shandong Province, China
Cheng-Yan Wang, Human Phenome Institute, Fudan University, Shanghai 201203, China
Author contributions: Li GY, Wang CY and Lv J collected and analyzed the references mentioned in the review; Li GY wrote the manuscript; Wang CY and Lv J revised the manuscript; all authors have read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 61902338 and No. 62001120; and Shanghai Sailing Program, No. 20YF1402400.
Conflict-of-interest statement: Authors have no conflict-of-interest to declare.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Cheng-Yan Wang, PhD, Associate Professor, Human Phenome Institute, Fudan University, No. 825 Zhangheng Road, Pudong New District, Shanghai 201203, China. wangcy@fudan.edu.cn
Received: May 24, 2021
Peer-review started: May 24, 2021
First decision: June 16, 2021
Revised: June 24, 2021
Accepted: August 17, 2021
Article in press: August 17, 2021
Published online: August 28, 2021
Core Tip

Core Tip: We summarized the current deep learning-based abdominal image reconstruction methods in this review. The deep learning reconstruction methods can solve the issues of slow imaging speed in magnetic resonance imaging and high-dose radiation in computed tomography while maintaining high image quality. Deep learning has a wide range of clinical applications in current abdominal imaging.