L1 and total variation regularized C-arm cardiac cone beam CT reconstruction

2013 ◽  
Author(s):  
Bo Liu ◽  
Fugen Zhou
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction

scholarly journals GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

2010 ◽  
Vol 37 (4) ◽  
pp. 1757-1760 ◽  
Author(s):  
Xun Jia ◽  
Yifei Lou ◽  
Ruijiang Li ◽  
William Y. Song ◽  
Steve B. Jiang
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Noisy Projection

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.

High quality 4D cone-beam CT reconstruction using motion-compensated total variation regularization

2017 ◽  
Vol 62 (8) ◽  
pp. 3313-3329 ◽  
Author(s):  
Hua Zhang ◽  
Jianhua Ma ◽  
Zhaoying Bian ◽  
Dong Zeng ◽  
Qianjin Feng ◽  
...  
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Total Variation Regularization ◽  
Ct Reconstruction ◽  
High Quality

scholarly journals GPU-based fast low-dose cone beam CT reconstruction via total variation

2011 ◽  
Vol 19 (2) ◽  
pp. 139-154 ◽  
Author(s):  
Xun Jia ◽  
Yifei Lou ◽  
John Lewis ◽  
Ruijiang Li ◽  
Xuejun Gu ◽  
...  
Keyword(s):  
Total Variation ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction

scholarly journals WE-E-201B-01: GPU-Based Fast Cone Beam CT Reconstruction from Undersampled and Noisy Projection Data Via Total Variation

2010 ◽  
Vol 37 (6Part6) ◽  
pp. 3441-3441 ◽  
Author(s):  
X Jia ◽  
Y Lou ◽  
J Lewis ◽  
R Li ◽  
X Gu ◽  
...  
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Noisy Projection

scholarly journals GPU-based Fast Low Dose Cone Beam CT Reconstruction via Total Variation

2010 ◽  
Vol 78 (3) ◽  
pp. S45 ◽  
Author(s):  
X. Jia ◽  
Y. Lou ◽  
R. Li ◽  
J. Lewis ◽  
C. Men ◽  
...  
Keyword(s):  
Total Variation ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction

Dental cone-beam CT reconstruction from limited-angle view data based on compressed-sensing (CS) theory for fast, low-dose X-ray imaging

2014 ◽  
Vol 64 (12) ◽  
pp. 1907-1911
Author(s):  
Uikyu Je ◽  
Hyosung Cho ◽  
Minsik Lee ◽  
Jieun Oh ◽  
Yeonok Park ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
X Ray ◽  
X Ray Imaging ◽  
Limited Angle
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