Preliminary study of region-of-interest image reconstruction with intensity weighting in cone-beam CT using iterative algorithm

2014 ◽  
Author(s):  
Kihong Son ◽  
Jiseoc Lee ◽  
Younjeong Lee ◽  
Jin Sung Kim ◽  
Seungryong Cho
Keyword(s):  
Image Reconstruction ◽  
Iterative Algorithm ◽  
Cone Beam Ct ◽  
Region Of Interest ◽  
Cone Beam ◽  
Preliminary Study

Chord-based image reconstruction in cone-beam CT with a curved detector

2006 ◽  
Vol 33 (10) ◽  
pp. 3743-3757 ◽  
Author(s):  
Nianming Zuo ◽  
Dan Xia ◽  
Yu Zou ◽  
Tianzi Jiang ◽  
Xiao-Chuan Pan
Keyword(s):  
Image Reconstruction ◽  
Cone Beam Ct ◽  
Cone Beam

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.

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