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Variant Wasserstein Generative Adversarial Network Applied on low dose ct Image Denoising

Computers, Materials & Continua 2023年第5期75卷 4535-4552页

作者： Anoud A.Mahmoud Hanaa A.Sayed Sara S.Mohamed Department of Mathematics and Computer Science Faculty of ScienceNew Valley UniversityNew Valley72511Egypt Department of Computer Science College of Computer Science and Engineering Taibah UniversityMedina42221KSA Department of Mathematics Faculty of ScienceAssiut UniversityAssiut71516Egypt

Computed Tomography(ct)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are *** the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a low-dose ct(LDct)image may cause more noise and increased artifacts,which can compromise the ***,image reconstruction from LDct image data is necessary to improve radiologists’judgment and *** study proposed three novel models for denoising LDct images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDct images and investigate the effect of different types of loss ***,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed *** results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and *** proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 *** prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.

关键词： Machine learning deep learning image denoising low dose ct loss function

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Deep leaning for low-dose ct image combined with anatomical prior information

Deep leaning for low-dose CT image combined with anatomical ...

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第十七届中国体视学与图像分析学术会议

作者：黄振兴 CHEN Zixiang ZHANG Qiyang LIU Xinfeng WANG Rongpin LIU Xin ZHENG Hairong LIANG Dong 胡战利 Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Health Informatics Department of Radiology Guizhou Provincial People's Hospital

Background dose reduction in computed tomography(ct) has gained considerable attention in clinical applications because it decreases radiation *** suppress noise and artifacts caused by reduced radiation,several deep learning(DL)-based image restoration methods have been proposed to improve diagnostic *** of these popular DL-based methods adopt an encoder-decoder ***,this popular framework may suffer from information loss for continual down-sampling *** the other hand,most of them disregard the anatomical differences among human body *** In this study,we primarily introduce an optimized model based on the popular encoderdecoder ***,a novel denoising approach by introducing information of the anatomical prior are proposed in Figure 1(a) and(b).For the first optimized model,several optimization strategies are engaged,for example,optimizing the network architecture by increasing attention module and introducing multiple loss functions so as to improve visual *** the other hand,a unified network framework for the denoising model is employed to process anatomical *** anatomical prior is represented as a pattern of weights of the features extracted from the corresponding *** Experimental results demonstrate that our method,which incorporates anatomical prior information,is effective in denoising in Figure.1(c).The quantitative results also prove the superiority of proposed methods in terms of evaluation metrics,including PSNR and *** Compared with the traditional networks that do not consider the anatomical prior and whose weights are consequently the same for each anatomical site,the proposed network achieves better performance by adaptively adjusting to the anatomical prior information.

关键词： low dose ct deep learning anatomical prior information denoising CNN

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