Image reconstruction from rebinned helical cone-beam projection data

2007 ◽  
Author(s):  
Dan Xia ◽  
Lifeng Yu ◽  
Junguo Bian ◽  
Xiaochuan Pan
Keyword(s):  
Image Reconstruction ◽  
Cone Beam ◽  
Projection Data ◽  
Beam Projection

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.

scholarly journals Data correlation based noise level estimation for cone beam projection data

2017 ◽  
Vol 25 (6) ◽  
pp. 907-926 ◽  
Author(s):  
Ti Bai ◽  
Hao Yan ◽  
Luo Ouyang ◽  
David Staub ◽  
Jing Wang ◽  
...  
Keyword(s):  
Noise Level ◽  
Cone Beam ◽  
Projection Data ◽  
Data Correlation ◽  
Noise Level Estimation ◽  
Beam Projection

Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data

2004 ◽  
Vol 49 (24) ◽  
pp. 5489-5503 ◽  
Author(s):  
Tingliang Zhuang ◽  
Shuai Leng ◽  
Brian E Nett ◽  
Guang-Hong Chen
Keyword(s):  
Image Reconstruction ◽  
Cone Beam ◽  
Projection Data ◽  
Beam Image

scholarly journals A General Local Reconstruction Approach Based on a Truncated Hilbert Transform

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Yangbo Ye ◽  
Hengyong Yu ◽  
Yuchuan Wei ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Hilbert Transform ◽  
Region Of Interest ◽  
Cone Beam ◽  
Projection Data ◽  
Partial Knowledge ◽  
Practical Applications ◽  
Beam Geometry ◽  
Volume Of Interest ◽  
Simulation Results

Exact image reconstruction from limited projection data has been a central topic in the computed tomography (CT) field. In this paper, we present a general region-of-interest/volume-of-interest (ROI/VOI) reconstruction approach using a truly truncated Hilbert transform on a line-segment inside a compactly supported object aided by partial knowledge on one or both neighboring intervals of that segment. Our approach and associated new data sufficient condition allows the most flexible ROI/VOI image reconstruction from the minimum account of data in both the fan-beam and cone-beam geometry. We also report primary numerical simulation results to demonstrate the correctness and merits of our finding. Our work has major theoretical potentials and innovative practical applications.

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