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
Abstract

Computed tomography (CT) has seen a rapid increase in use in recent years. Radiation from CT accounts for a significant proportion of total medical radiation. However, given the known harmful impact of radiation exposure to the human body, the excessive use of CT in medical environments raises concerns. Concerns over increasing CT use and its associated radiation burden have prompted efforts to reduce radiation dose during the procedure. Therefore, low-dose CT has attracted major attention in the radiology, since CT-associated x-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Therefore, several denoising methods have been developed and applied to image processing technologies with the goal of reducing image noise. Recently, deep learning applications that improve image quality by reducing the noise and artifacts have become commercially available for diagnostic imaging. Deep learning image reconstruction shows great potential as an advanced reconstruction method to improve the quality of clinical CT images. These improvements can provide significant benefit to patients regardless of their disease, and further advances are expected in the near future.

Keywords: 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.