Publication Name	World Journal of Clinical Cases
Manuscript ID	65828
Country	South Korea

Received	2021-03-16 14:28
Peer-Review Started	2021-03-16 14:32
To Make the First Decision
Return for Revision	2021-04-24 03:18
Revised	2021-05-12 15:41
Second Decision	2021-08-17 03:18
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2021-08-17 13:23
Articles in Press	2021-08-17 13:23
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-09-06 06:54
Publish the Manuscript Online	2021-09-10 07:23

ISSN	2307-8960 (online)
Open Access	eviewed 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	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Editorial
Article Title	Advances in deep learning for computed tomography denoising
Manuscript Source	Invited Manuscript
All Author List	Sung Bin Park
ORCID	Author(s) ORCID Number Sung Bin Park http://orcid.org/0000-0002-4155-9260
Funding Agency and Grant Number
Corresponding Author	Sung Bin Park, MD, PhD, Chief Physician, Full Professor, Department of Radiology, Chung-Ang University Hospital, 102, Heukseok-ro, Dongjak-gu, Seoul 06973, South Korea. pksungbin@paran.com
Key Words	Denoising; Deep learning; Computer-assisted imaging processing; Iterative reconstruction; Radiation dose
Core Tip	Early application of deep learning techniques have shown success in the denoising of computed tomography (CT) images, especially low-dose CT images, and future advances are expected to provide additional benefit.
Publish Date	2021-09-10 07:23
Citation	Park SB. Advances in deep learning for computed tomography denoising. World J Clin Cases 2021; 9(26): 7614-7619
URL	https://www.wjgnet.com/2307-8960/full/v9/i26/7614.htm
DOI	https://dx.doi.org/10.12998/wjcc.v9.i26.7614

Full Article (PDF)	WJCC-9-7614.pdf
Full Article (Word)	WJCC-9-7614.docx
Manuscript File	65828_Auto_Edited-ZMG.docx
Answering Reviewers	65828-Answering reviewers.pdf
Audio Core Tip	65828-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	65828-Conflict-of-interest statement.pdf
Copyright License Agreement	65828-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	65828-Language certificate.pdf
Peer-review Report	65828-Peer-review(s).pdf
Scientific Misconduct Check	65828-Scientific misconduct check.pdf
Scientific Editor Work List	65828-Scientific editor work list.pdf
CrossCheck Report	65828-CrossCheck report.pdf