Editorial
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Sep 16, 2021; 9(26): 7614-7619
Published online Sep 16, 2021. doi: 10.12998/wjcc.v9.i26.7614
Advances in deep learning for computed tomography denoising
Sung Bin Park
Sung Bin Park, Department of Radiology, Chung-Ang University Hospital, Seoul 06973, South Korea
Author contributions: Park SB solely contributed to this paper.
Conflict-of-interest statement: The author has no conflicts of interest.
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: 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
Received: March 16, 2021
Peer-review started: March 16, 2021
First decision: April 24, 2021
Revised: May 12, 2021
Accepted: August 17, 2021
Article in press: August 17, 2021
Published online: September 16, 2021
Processing time: 177 Days and 16.9 Hours
Core Tip

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.