scholarly journals Validation of Dark Band Artifact Reduction Using Artifact Reduction Processing Methods with Low-dose CT Images: Phantom Study

2015 ◽  
Vol 71 (4) ◽  
pp. 316-324
Author(s):  
Masaaki Fukunaga ◽  
Hideo Onishi ◽  
Hiroyuki Yamamoto
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Phantom Study ◽  
Dark Band ◽  
Artifact Reduction ◽  
Processing Methods ◽  
Low Dose Ct

A computer simulation method for low-dose CT images by use of real high-dose images: a phantom study

2015 ◽  
Vol 9 (1) ◽  
pp. 44-52 ◽  
Author(s):  
Tomomi Takenaga ◽  
Shigehiko Katsuragawa ◽  
Makoto Goto ◽  
Masahiro Hatemura ◽  
Yoshikazu Uchiyama ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Low Dose ◽  
Simulation Method ◽  
Ct Images ◽  
Phantom Study ◽  
High Dose ◽  
Computer Simulation Method ◽  
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

scholarly journals Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks

2019 ◽  
Vol 46 (3) ◽  
pp. 1286-1299 ◽  
Author(s):  
Peirui Bai ◽  
Jayaram K. Udupa ◽  
Yubing Tong ◽  
ShiPeng Xie ◽  
Drew A. Torigian
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Whole Body ◽  
Ct Scans ◽  
Body Region ◽  
Low Dose Ct ◽  
Pet Ct

Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images

2019 ◽  
Vol 66 (9) ◽  
pp. 2641-2650 ◽  
Author(s):  
Muhammad Nadeem Cheema ◽  
Anam Nazir ◽  
Bin Sheng ◽  
Ping Li ◽  
Jing Qin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Low Dose ◽  
Ct Images ◽  
Low Dose Ct ◽  
Convolution Neural Networks
Sign in / Sign up

Export Citation Format

Share Document