Low-dose CT reconstruction with simultaneous sinogram and image domain denoising by deep neural network

2020 ◽  
Author(s):  
Jiongtao Zhu ◽  
Ting Su ◽  
Xiaolei Deng ◽  
Xindong Sun ◽  
Hairong Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Dose ◽  
Ct Reconstruction ◽  
Image Domain ◽  
Low Dose Ct

scholarly journals Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction

2019 ◽  
Vol 1 (6) ◽  
pp. 269-276 ◽  
Author(s):  
Hongming Shan ◽  
Atul Padole ◽  
Fatemeh Homayounieh ◽  
Uwe Kruger ◽  
Ruhani Doda Khera ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Deep Neural Network ◽  
Low Dose ◽  
Ct Image ◽  
Competitive Performance ◽  
Low Dose Ct ◽  
Ct Image Reconstruction

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging

2021 ◽  
Vol 29 (1) ◽  
pp. 91-109
Author(s):  
Zhiwei Feng ◽  
Ailong Cai ◽  
Yizhong Wang ◽  
Lei Li ◽  
Li Tong ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Dose ◽  
Structural Information ◽  
Superior Performance ◽  
Projection Data ◽  
Image Domain ◽  
Low Dose Ct ◽  
Quantitative Metrics ◽  
Complementary Role ◽  
Ct Technology

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affecting the diagnosis. Therefore, in this study, we investigate a dual residual convolution neural network (DRCNN) for low-dose CT (LDCT) imaging, whereby the CT images are reconstructed directly from the sinogram by integrating analytical domain transformations, thus reducing the loss of projection information. With this new framework, feature extraction is performed simultaneously on both the sinogram-domain sub-net and the image-domain sub-net, which utilize the residual shortcut networks and play a complementary role in suppressing the projection noise and reducing image error. This new DRCNN approach helps not only decrease the sinogram noise but also preserve significant structural information. The experimental results of simulated and real projection data demonstrate that our DRCNN achieve superior performance over other state-of-art methods in terms of visual inspection and quantitative metrics. For example, comparing with RED-CNN and DP-ResNet, the value of PSNR using our DRCNN is improved by nearly 3 dB and 1 dB, respectively.

scholarly journals Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network

2017 ◽  
Vol 36 (12) ◽  
pp. 2479-2486 ◽  
Author(s):  
Dufan Wu ◽  
Kyungsang Kim ◽  
Georges El Fakhri ◽  
Quanzheng Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct ◽  
Artificial Neural

scholarly journals Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 41839-41855 ◽  
Author(s):  
Chenyu You ◽  
Wenxiang Cong ◽  
Ge Wang ◽  
Qingsong Yang ◽  
Hongming Shan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Dose ◽  
Low Dose Ct ◽  
Multi Scale

Median prior constrained TV algorithm for sparse view low-dose CT reconstruction

2015 ◽  
Vol 60 ◽  
pp. 117-131 ◽  
Author(s):  
Yi Liu ◽  
Hong Shangguan ◽  
Quan Zhang ◽  
Hongqing Zhu ◽  
Huazhong Shu ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct

scholarly journals Low-dose CT reconstruction using spatially encoded nonlocal penalty

2017 ◽  
Vol 44 (10) ◽  
pp. e376-e390 ◽  
Author(s):  
Kyungsang Kim ◽  
Georges El Fakhri ◽  
Quanzheng Li
Keyword(s):  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct

Weighted adaptive non-local dictionary for low-dose CT reconstruction

2021 ◽  
Vol 180 ◽  
pp. 107871
Author(s):  
Haijun Yu ◽  
Shaoyu Wang ◽  
Weiwen Wu ◽  
Changcheng Gong ◽  
Linbo Wang ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct ◽  
Non Local
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