Li GY, Wang CY, Lv J. Current status of deep learning in abdominal image reconstruction. Artif Intell Med Imaging 2021; 2(4): 86-94 [DOI: 10.35711/aimi.v2.i4.86]
Corresponding Author of This Article
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
Research Domain of This Article
Engineering, Biomedical
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
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 byNational 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 Processing time: 97 Days and 17.4 Hours
Abstract
Abdominal magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used for disease screening, diagnosis, and treatment guidance. However, abdominal MRI has disadvantages including slow speed and vulnerability to motions, while CT suffers from problems of radiation. It has been reported that deep learning reconstruction can solve such problems while maintaining good image quality. Recently, deep learning-based image reconstruction has become a hot topic in the field of medical imaging. This study reviews the latest research on deep learning reconstruction in abdominal imaging, including the widely used convolutional neural network, generative adversarial network, and recurrent neural network.
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.
