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 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

A new weighting scheme for arc based circle cone-beam CT reconstruction

2021 ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Xiang-Gen Xia ◽  
Chuanjiang He ◽  
Zemin Ren ◽  
Jian Lu
Keyword(s):  
Characteristic Function ◽  
Weighting Function ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Characteristic Functions ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Scanning Angle ◽  
Beam Geometry

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich’s helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ ( x _ , λ ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x _ . In fact, for every pixel x _ , our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software “ASTRA toolbox.” We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

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 Low-Dose Cone-Beam CT Iterative Reconstruction via Total Variation and Gradient Total Variation

2021 ◽  
Author(s):  
Junlong Cui ◽  
Gang Yu
Keyword(s):  
Total Variation ◽  
Noise Suppression ◽  
Mean Squared Error ◽  
Cone Beam Ct ◽  
Structural Similarity ◽  
Cone Beam ◽  
Spatial Gradient ◽  
Projection Data ◽  
New Strategy ◽  
Ill Posed

Abstract The compressed sensing (CS) technique has been utilized to reconstruct Cone-beam computed tomography (CBCT) images via limited projection from under-sampled measurements. However, the condition of limited projection is an ill-posed problem. Since the CBCT image itself doesn’t have sparse features, the total variation (TV) transform has been widely adopted in CBCT reconstruction. This method, which penalizes the weight of each voxel at a constant rate regardless of different spatial gradient, may not recover qualified CBCT images from ill-posed projection data. This work presents a new strategy to deal with the deficits stated above by utilizing non-uniform weighting penalization in CBCT reconstruction. The proposed new strategy combines TV and gradient total variation (GTV) for reconstruction in a hybrid weighting penalization way, where the total variation is penalized by the gradient total variation in advance. The proposed penalty not only retains the benefits of TV, including artifact and noise suppression, but also maintains the structures in regions with gradual gradient intensity transition more effectively. This study tested the proposed method by under-sampled projections of 2 objects and 2 experiments (2 digital phantom). We assessed its performance against the OS-SART method, FDK method, conventional TV method and TV+GTV method in the tissue contrast, reconstruction accuracy, and imaging resolution by comparing the root mean squared error (RMSE), the correlation coefficient (CC), the structural similarity (SSIM), and profiles intensity of the reconstructed images. The proposed method produced the reconstructed image with the lowest RMSEs and the highest CCs and SSIMs for each experiment.

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
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