Imaging performance evaluation and analysis of intelligent low-dose CT image denoising algorithms
10.3969/j.issn.1005-202X.2025.05.010
- VernacularTitle:面向低剂量CT智能去噪算法的成像性能评估与分析
- Author:
Menghuang WEN
1
;
Ximing CAO
1
;
Zhaoying BIAN
1
;
Jianhua MA
1
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510515
- Publication Type:Journal Article
- Keywords:
low-dose CT;
denoising network;
image-domain;
projection-image dual-domain
- From:
Chinese Journal of Medical Physics
2025;42(5):620-624
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the low-dose CT image denoising and generalization performance of the existing mainstream deep learning based denoising networks.Methods The public AAPM Mayo challenge dataset was used to train the denoising network using 3 image-domain methods(REDCNN,WGAN-VGG,CTformer)and 2 projection-image dual-domain methods(VVBP-UNet,CLEAR),separately.The denoising networks were evaluated quantitatively for peak signal-to-noise ratio(PSNR),structural similarity index,root mean square error,number of network parameters and floating point operations,and their generalization performance was analyzed on the AbdomenCT-1K Dataset.Results Image-domain denoising networks effectively suppressed low-dose CT image noise,with REDCNN demonstrating the best denoising performance and achieving a PSNR of 42.0988 dB.The dual-domain denoising networks were better at preserving tiny tissue structures while removing image noise,with VVBP-UNet performing the best and increasing PSNR to 42.150 9 dB.Conclusion The projection-image dual-domain method exhibits superior denoising and generalization performances than the image-domain method,despite requiring a relatively large amount of network parameters and computations.When computing resources are sufficient,the denoising results obtained by dual-domain method better fulfill the requirements for clinical diagnosis.
