Journal of Artificial Intelligence for Medical Sciences

Volume 2, Issue 1-2, June 2021, Pages 33 - 43

Deep High-Resolution Network for Low-Dose X-Ray CT Denoising

Authors
Ti BaiORCID, Dan NguyenORCID, Biling Wang, Steve Jiang*
Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
*Corresponding author. Email: Steve.Jiang@UTSouthwestern.edu
Corresponding Author
Steve Jiang
Received 31 January 2021, Accepted 15 April 2021, Available Online 3 May 2021.
DOI
https://doi.org/10.2991/jaims.d.210428.001How to use a DOI?
Keywords
Low-dose CT, Deep learning, Denoise
Abstract

Low-dose computed tomography (LDCT) is clinically desirable because it reduces the radiation dose to patients. However, the quality of LDCT images is often suboptimal because of the inevitable strong quantum noise. Because of their unprecedented success in computer vision, deep learning (DL)-based techniques have been used for LDCT denoising. Despite DL models' promising ability to remove noise, researchers have observed that the resolution of DL-denoised images is compromised, which decreases their clinical value. To mitigate this problem, in this work, we developed a more effective denoiser by introducing a high-resolution network (HRNet). HRNet consists of multiple branches of subnetworks that extract multiscale features that are fused together later, which substantially enhances the quality of the generated features and improves denoising performance. Experimental results demonstrated that the introduced HRNet-based denoiser outperformed the benchmarked U-Net–based denoiser, as it provided superior image resolution preservation and comparable, if not better, noise suppression. Quantitative evaluation in terms of root-mean-squared errors (RMSEs)/structure similarity index (SSIM) showed that the HRNet-based denoiser improve these values from 113.80/0.550 (LDCT) to 55.24/0.745 (HRNet), which outperformed the 59.87/0.712 for the U-Net–based denoiser.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Artificial Intelligence for Medical Sciences
Volume-Issue
2 - 1-2
Pages
33 - 43
Publication Date
2021/05/03
ISSN (Online)
2666-1470
DOI
https://doi.org/10.2991/jaims.d.210428.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Ti Bai
AU  - Dan Nguyen
AU  - Biling Wang
AU  - Steve Jiang
PY  - 2021
DA  - 2021/05/03
TI  - Deep High-Resolution Network for Low-Dose X-Ray CT Denoising
JO  - Journal of Artificial Intelligence for Medical Sciences
SP  - 33
EP  - 43
VL  - 2
IS  - 1-2
SN  - 2666-1470
UR  - https://doi.org/10.2991/jaims.d.210428.001
DO  - https://doi.org/10.2991/jaims.d.210428.001
ID  - Bai2021
ER  -