scholarly journals Ultra‐low‐dose PET reconstruction using generative adversarial network with feature matching and task‐specific perceptual loss

2019 ◽  
Vol 46 (8) ◽  
pp. 3555-3564 ◽  
Author(s):  
Jiahong Ouyang ◽  
Kevin T. Chen ◽  
Enhao Gong ◽  
John Pauly ◽  
Greg Zaharchuk
Keyword(s):  
Low Dose ◽  
Feature Matching ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Pet Reconstruction ◽  
And Task

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

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 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 Unpaired Image Denoising via Wasserstein GAN in Low-Dose CT Image with Multi-Perceptual Loss and Fidelity Loss

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 126
Author(s):  
Zhixian Yin ◽  
Kewen Xia ◽  
Ziping He ◽  
Jiangnan Zhang ◽  
Sijie Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Denoising ◽  
Low Dose ◽  
Radiation Risk ◽  
Semantic Features ◽  
Original Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
The Difference

The use of low-dose computed tomography (LDCT) in medical practice can effectively reduce the radiation risk of patients, but it may increase noise and artefacts, which can compromise diagnostic information. The methods based on deep learning can effectively improve image quality, but most of them use a training set of aligned image pairs, which are difficult to obtain in practice. In order to solve this problem, on the basis of the Wasserstein generative adversarial network (GAN) framework, we propose a generative adversarial network combining multi-perceptual loss and fidelity loss. Multi-perceptual loss uses the high-level semantic features of the image to achieve the purpose of noise suppression by minimizing the difference between the LDCT image and the normal-dose computed tomography (NDCT) image in the feature space. In addition, L2 loss is used to calculate the loss between the generated image and the original image to constrain the difference between the denoised image and the original image, so as to ensure that the image generated by the network using the unpaired images is not distorted. Experiments show that the proposed method performs comparably to the current deep learning methods which utilize paired image for image denoising.

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
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