Zhou, Yusheng, Kong, Zhengmin, Huang, Tao, Ahn, Euijoon, Li, Hao, and Ding, Li (2024) WaveletDFDS-Net: A Dual Forward Denoising Stream Network for Low-Dose CT Noise Reduction. Electronics, 13. 1906.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

The challenge of denoising low-dose computed tomography (CT) has garnered significant research interest due to the detrimental impact of noise on CT image quality, impeding diagnostic accuracy and image-guided therapies. This paper introduces an innovative approach termed the Wavelet Domain Dual Forward Denoising Stream Network (WaveletDFDS-Net) to address this challenge. This method ingeniously combines convolutional neural networks and Transformers to leverage their complementary capabilities in feature extraction. Additionally, it employs a wavelet transform for efficient image downsampling, thereby preserving critical information while reducing computational requirements. Moreover, we have formulated a distinctive dual-domain compound loss function that significantly enhances the restoration of intricate details. The performance of WaveletDFDS-Net is assessed by comparative experiments conducted on public CT datasets, and results demonstrate its enhanced denoising effect with an SSIM of 0.9269, PSNR of 38.1343 and RMSE of 0.0130, superior to existing methods.

Item ID:	82827
Item Type:	Article (Research - C1)
ISSN:	2079-9292
Keywords:	computerized tomography denoising; wavelet transform; convolution operation; vision transformer; deep learning
Copyright Information:	© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Funders:	Australian Research Council (ARC)
Projects and Grants:	ARC DP220101634
Date Deposited:	21 May 2024 06:31
FoR Codes:	32 BIOMEDICAL AND CLINICAL SCIENCES > 3299 Other biomedical and clinical sciences > 329999 Other biomedical and clinical sciences not elsewhere classified @ 30% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 70%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280103 Expanding knowledge in the biomedical and clinical sciences @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 70%
Downloads:	Total: 16 Last 12 Months: 12
	More Statistics