A parallel implementation of 3D CT image reconstruction on the Cell Broadband Engine

2010 ◽  
Vol 24 (2) ◽  
pp. 117-127 ◽  
Author(s):  
M. Sakamoto ◽  
M. Murase
Keyword(s):  
Image Reconstruction ◽  
Parallel Implementation ◽  
Ct Image ◽  
3D Ct ◽  
Cell Broadband Engine ◽  
Ct Image Reconstruction

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

A Parallel Implementation of the Katsevich Algorithm for 3-D CT Image Reconstruction

2006 ◽  
Vol 38 (1) ◽  
pp. 35-47 ◽  
Author(s):  
Junjun Deng ◽  
Hengyong Yu ◽  
Jun Ni ◽  
Tao He ◽  
Shiying Zhao ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Parallel Implementation ◽  
Ct Image ◽  
Katsevich Algorithm ◽  
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.

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