A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising  

作　　者：Chaoqun Tan Mingming Yang Zhisheng You Hu Chen Yi Zhang 

机构地区：[1]National Key Laboratory of Fundamental Science on Synthetic Vision,Sichuan University,Chengdu 610065,China [2]College of Computer Science,Sichuan University,Chengdu 610065,China

出　　处：《Precision Clinical Medicine》2022年第2期125-136,共12页精准临床医学（英文）

基　　金：funded by the National Natural Science Foundation of China(Grants No.61871277 and 61671312);in part by the Project of State Administration of Traditional Chinese Medicine of Sichuan(Grant No.2021MS012).

摘　　要：Low-dose computed tomography(LDCT)denoising is an indispensable procedure in the medical imaging field,which not only improves image quality,but can mitigate the potential hazard to patients caused by routine doses.Despite the improvement in performance of the cycle-consistent generative adversarial network(CycleGAN)due to the well-paired CT images shortage,there is still a need to further reduce image noise while retaining detailed features.Inspired by the residual encoder–decoder convolutional neural network(RED-CNN)and U-Net,we propose a novel unsupervised model using CycleGAN for LDCT imaging,which injects a two-sided network into selective kernel networks(SK-NET)to adaptively select features,and uses the patchGAN discriminator to generate CT images with more detail maintenance,aided by added perceptual loss.Based on patch-based training,the experimental results demonstrated that the proposed SKFCycleGAN outperforms competing methods in both a clinical dataset and the Mayo dataset.The main advantages of our method lie in noise suppression and edge preservation.

关 键 词：cycle-consistent adversarial network selective kernel networks unsupervised low dose CT image denoising clinical dataset 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]