A feasibility study of extracting tissue textures from a previous normal-dose CT database as prior for Bayesian reconstruction of current ultra-low-dose CT images

2018 ◽  
Author(s):  
Zhengrong Jerome Liang ◽  
Yongfeng Gao ◽  
Yuxiang Xing ◽  
William H. Moore ◽  
Jianhua Ma ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Low Dose ◽  
Ct Images ◽  
Bayesian Reconstruction ◽  
Normal Dose ◽  
Low Dose Ct

scholarly journals Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images

2016 ◽  
Vol 35 (3) ◽  
pp. 860-870 ◽  
Author(s):  
Hao Zhang ◽  
Hao Han ◽  
Zhengrong Liang ◽  
Yifan Hu ◽  
Yan Liu ◽  
...  
Keyword(s):  
Ct Scan ◽  
Low Dose ◽  
Ct Images ◽  
Bayesian Reconstruction ◽  
Full Dose ◽  
Low Dose Ct ◽  
Knowledge Based

Construction of virtual normal dose CT images from ultra-low dose CT images using dilated residual networks

2020 ◽  
Author(s):  
Akiyoshi Hizukuri ◽  
Ryohei Nakayama ◽  
Yasutaka Ichikawa ◽  
Motonori Nagata ◽  
Masaki Ishida ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Normal Dose ◽  
Low Dose Ct

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
Sign in / Sign up

Export Citation Format

Share Document