scholarly journals PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction

2018 ◽  
Vol 37 (6) ◽  
pp. 1498-1510 ◽  
Author(s):  
Xuehang Zheng ◽  
Saiprasad Ravishankar ◽  
Yong Long ◽  
Jeffrey A. Fessler
Keyword(s):  
Image Reconstruction ◽  
Low Dose ◽  
Ct Image ◽  
3D Ct ◽  
Ct Image Reconstruction

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

scholarly journals Deep Encoder-Decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-View Data

2019 ◽  
Vol 6 (4) ◽  
pp. 111 ◽  
Author(s):  
Huidong Xie ◽  
Hongming Shan ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Radiation Dose ◽  
Image Data ◽  
Ct Image ◽  
3D Ct ◽  
X Ray ◽  
3D Network ◽  
Ct Image Reconstruction ◽  
3D Information ◽  
3D Volume

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.

scholarly journals Low-Dose CT Image Reconstruction With a Deep Learning Prior

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 158647-158655
Author(s):  
Hyoung Suk Park ◽  
Kyungsang Kim ◽  
Kiwan Jeon
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Low Dose ◽  
Ct Image ◽  
Low Dose Ct ◽  
Ct Image Reconstruction
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