scholarly journals Region of interest reconstruction from truncated data in circular cone-beam CT

2006 ◽  
Vol 25 (7) ◽  
pp. 869-881 ◽  
Author(s):  
Lifeng Yu ◽  
Yu Zou ◽  
E.Y. Sidky ◽  
C.A. Pelizzari ◽  
P. Munro ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Region Of Interest ◽  
Circular Cone ◽  
Cone Beam ◽  
Truncated Data

scholarly journals Region of interest reconstruction from truncated data in circular cone-beam CT

2005 ◽  
Author(s):  
Lifeng Yu ◽  
Dan Xia ◽  
Yu Zou ◽  
Xiaochuan Pan ◽  
Charles Pelizzari ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Region Of Interest ◽  
Circular Cone ◽  
Cone Beam ◽  
Truncated Data

scholarly journals Efficient reconstruction from truncated data in circular cone-beam CT

2013 ◽  
Vol 62 (9) ◽  
pp. 098702
Author(s):  
Wang Xian-Chao ◽  
Yan Bin ◽  
Liu Hong-Kui ◽  
Li Lei ◽  
Wei Xing ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Circular Cone ◽  
Cone Beam ◽  
Truncated Data

scholarly journals GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Xing Zhao ◽  
Jing-jing Hu ◽  
Peng Zhang
Keyword(s):  
Image Reconstruction ◽  
Cone Beam Ct ◽  
Large Data ◽  
Circular Cone ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Process ◽  
Data Volume ◽  
Ct Image Reconstruction

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.

Calibration for Circular Cone-Beam CT Based on Consistency Conditions

2017 ◽  
Vol 1 (6) ◽  
pp. 517-526 ◽  
Author(s):  
Jerome Lesaint ◽  
Simon Rit ◽  
Rolf Clackdoyle ◽  
Laurent Desbat
Keyword(s):  
Cone Beam Ct ◽  
Circular Cone ◽  
Cone Beam ◽  
Consistency Conditions
Sign in / Sign up

Export Citation Format

Share Document