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 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

Convergence and stability analysis of the half thresholding based few-view CT reconstruction

2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang
Keyword(s):  
Noise Suppression ◽  
Reconstructed Image ◽  
Reference Image ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Ill Posed ◽  
Thresholding Algorithm ◽  
The Stability ◽  
Well Posed

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

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 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 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.

TH-E-330A-05: Reducing Metal Artifacts in Cone-Beam CT by Tracking and Eliminating Metal Shadows in Raw Projection Data

2006 ◽  
Vol 33 (6Part23) ◽  
pp. 2288-2288 ◽  
Author(s):  
Y Zhang ◽  
L Zhang ◽  
RX Zhu ◽  
M Chambers ◽  
L Dong
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Metal Artifacts

WE-EF-207-08: Improve Cone Beam CT Using a Synchronized Moving Grid, An Inter-Projection Sensor Fusion and a Probability Total Variation Reconstruction

2015 ◽  
Vol 42 (6Part39) ◽  
pp. 3682-3682 ◽  
Author(s):  
H Zhang ◽  
L Ren ◽  
V Kong ◽  
Y Zhang ◽  
W Giles ◽  
...  
Keyword(s):  
Total Variation ◽  
Sensor Fusion ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Moving Grid

The Improvement of Katsevich Reconstruction Algorithm Based on Cone-Beam CT

2012 ◽  
Vol 239-240 ◽  
pp. 1148-1151
Author(s):  
Li Fang Wang
Keyword(s):  
Image Quality ◽  
Polynomial Interpolation ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Experimental Results ◽  
Cone Beam ◽  
Projection Data ◽  
Interpolation Function ◽  
Reconstruction Process ◽  
Derivatives Of

The Katsevich reconstruction algorithm based on cone-beam must compute the derivative of projection data in the reconstruction process, but projection data are discrete and haven’t derivative. So the derivatives of the polynomial interpolation function are as approximation of the derivative of projection data. To verify the effectiveness of this method, 3D Shepp-Logan model is reconstructed by the method and the average gradient is used to measure the clarity of image. The experimental results show that this method enables image clearer and improves image quality

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