Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	68394
Country	China

Received	2021-05-24 12:44
Peer-Review Started	2021-05-24 12:47
To Make the First Decision
Return for Revision	2021-06-16 01:19
Revised	2021-06-24 03:42
Second Decision	2021-08-17 03:24
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-08-17 13:59
Articles in Press	2021-08-17 13:59
Publication Fee Transferred
Edit the Manuscript by Language Editor	2021-08-27 07:29
Typeset the Manuscript	2021-08-30 01:38
Publish the Manuscript Online	2021-08-30 06:09

ISSN	2644-3260 (online)
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/
Copyright	© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
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Category	Engineering, Biomedical
Manuscript Type	Minireviews
Article Title	Current status of deep learning in abdominal image reconstruction
Manuscript Source	Invited Manuscript
All Author List	Guang-Yuan Li, Cheng-Yan Wang and Jun Lv
Funding Agency and Grant Number	Funding Agency Grant Number National Natural Science Foundation of China 61902338 National Natural Science Foundation of China 62001120 Shanghai Sailing Program 20YF1402400
Corresponding Author	Cheng-Yan Wang, PhD, Associate Professor, Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai 201203, China. wangcy@fudan.edu.cn
Key Words	Abdominal imaging; Reconstruction; Magnetic resonance imaging; Computer tomography; Deep learning
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 computer tomography while maintaining high image quality. Deep learning has a wide range of clinical applications in current abdominal imaging.
Publish Date	2021-08-30 06:09
Citation	Li GY, Wang CY, Lv J. Current status of deep learning in abdominal image reconstruction. Artif Intell Med Imaging 2021; 2(4): 86-94
URL	https://www.wjgnet.com/2644-3260/full/v2/i4/86.htm
DOI	https://dx.doi.org/10.35711/aimi.v2.i4.86

Full Article (PDF)	AIMI-2-86.pdf
Full Article (Word)	AIMI-2-86.docx
Manuscript File	68394_Auto_Edited-ZMG-Webster J-Clear.docx
Answering Reviewers	68394-Answering reviewers.pdf
Audio Core Tip	68394-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	68394-Conflict-of-interest statement.pdf
Copyright License Agreement	68394-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	68394-Grant application form(s).pdf
Non-Native Speakers of English Editing Certificate	68394-Language certificate.pdf
Peer-review Report	68394-Peer-review(s).pdf
Scientific Misconduct Check	68394-Bing-Gao CC-1.jpg
Scientific Misconduct Check	68394-Bing-Liu M-2.png
Scientific Misconduct Check	68394-Scientific misconduct check.pdf
Scientific Editor Work List	68394-Scientific editor work list.pdf