Iterative reconstruction of cone-beam CT data on a cluster

2007 ◽  
Author(s):  
Thomas M. Benson ◽  
Jens Gregor
Keyword(s):  
Iterative Reconstruction ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Data

Automatic reconstruction method for high-contrast panoramic image from dental cone-beam CT data

2019 ◽  
Vol 175 ◽  
pp. 205-214 ◽  
Author(s):  
Zhaoqiang Yun ◽  
Shuo Yang ◽  
Erliang Huang ◽  
Lei Zhao ◽  
Wei Yang ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Reconstruction Method ◽  
Panoramic Image ◽  
High Contrast ◽  
Ct Data

TU-EE-A2-03: Evaluation of Dose Calculation in KV-Cone Beam CT Data Sets

2005 ◽  
Vol 32 (6Part17) ◽  
pp. 2109-2109
Author(s):  
T Tücking ◽  
S Nill ◽  
U Oelfke
Keyword(s):  
Cone Beam Ct ◽  
Dose Calculation ◽  
Cone Beam ◽  
Data Sets ◽  
Ct Data

scholarly journals 3D Alternating Direction TV-Based Cone-Beam CT Reconstruction with Efficient GPU Implementation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ailong Cai ◽  
Linyuan Wang ◽  
Hanming Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
...  
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
Image Size ◽  
Numerical Accuracy ◽  
Alternating Direction ◽  
Heavy Burden ◽  
Ct Data ◽  
Gpu Implementation

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of256×256×256and 36 projections of the size of512×512.

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