Low-dose CT simulation with a generative adversarial network

2019 ◽  
Author(s):  
Hongming Shan ◽  
Xun Jia ◽  
Klaus Mueller ◽  
Uwe Kruger ◽  
Ge Wang
Keyword(s):  
Low Dose ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Ct Simulation

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

The Gated Recurrent Conditional Generative Adversarial Network (GRC-GAN): application to denoising of low-dose CT images

2021 ◽  
Author(s):  
Mateus Baltazar de Almeida ◽  
Luis F. Alves Pereira ◽  
Tsang Ing Ren ◽  
George D. C. Cavalcanti ◽  
Jan Sijbers
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network

scholarly journals Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

2018 ◽  
Vol 37 (6) ◽  
pp. 1348-1357 ◽  
Author(s):  
Qingsong Yang ◽  
Pingkun Yan ◽  
Yanbo Zhang ◽  
Hengyong Yu ◽  
Yongyi Shi ◽  
...  
Keyword(s):  
Image Denoising ◽  
Low Dose ◽  
Wasserstein Distance ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network

scholarly journals High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 930-943 ◽  
Author(s):  
Linlin Yang ◽  
Hong Shangguan ◽  
Xiong Zhang ◽  
Anhong Wang ◽  
Zefang Han
Keyword(s):  
Image Denoising ◽  
High Frequency ◽  
Low Dose ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network

scholarly journals Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhihua Li ◽  
Weili Shi ◽  
Qiwei Xing ◽  
Yu Miao ◽  
Wei He ◽  
...  
Keyword(s):  
Loss Function ◽  
Low Dose ◽  
Structural Similarity ◽  
Image Method ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Quantitative Measurements ◽  
Better Than

The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

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