TU-B-201B-03: CT Reconstruction from Undersampled Projection Data Via Edge-Preserving Total Variation Regularization

2010 ◽  
Vol 37 (6Part6) ◽  
pp. 3378-3378 ◽  
Author(s):  
Z Tian ◽  
J Xun ◽  
K Yuan ◽  
S Jiang
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Edge Preserving

scholarly journals Low-dose CT reconstruction via edge-preserving total variation regularization

2011 ◽  
Vol 56 (18) ◽  
pp. 5949-5967 ◽  
Author(s):  
Zhen Tian ◽  
Xun Jia ◽  
Kehong Yuan ◽  
Tinsu Pan ◽  
Steve B Jiang
Keyword(s):  
Total Variation ◽  
Low Dose ◽  
Total Variation Regularization ◽  
Ct Reconstruction ◽  
Edge Preserving ◽  
Low Dose Ct

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 An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Luzhen Deng ◽  
Peng Feng ◽  
Mianyi Chen ◽  
Peng He ◽  
Biao Wei
Keyword(s):  
Total Variation ◽  
Locally Linear Embedding ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Minimization Method ◽  
Total Variation Minimization ◽  
Image Objects

Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method.

scholarly journals Multidirectional Anisotropic Total Variation and Its Application in the Tomography of the Surrounding Rock of Coal Mining Faces

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bo Chen ◽  
Guowei Zhu ◽  
Zhenqiang Yang
Keyword(s):  
Coal Mining ◽  
Total Variation ◽  
Regularization Method ◽  
Surrounding Rock ◽  
Total Variation Regularization ◽  
Ct Reconstruction ◽  
Traveltime Tomography ◽  
Transmitted Waves ◽  
Mining Face ◽  
Anisotropic Total Variation

The computed tomography (CT) reconstruction algorithm is one of the crucial components of the CT system. To date, total variation (TV) has been widely used in CT reconstruction algorithms. Although TV utilizes the a priori information of the longitudinal and lateral gradient sparsity of an image, it introduces some staircase artifacts. To overcome the current limitations of TV and improve imaging quality, we propose a multidirectional anisotropic total variation (MATV) that uses multidirectional gradient information. The surrounding rock of coal mining faces uses principles of tomography similar to those of medical X-rays. The velocity distribution for the surrounding rock can be obtained by the first-arrival traveltime tomography of the transmitted waves in the coal mining face. Combined with the geological data, we can interpret the geological hazards in the coal mining face. To perform traveltime tomography, we first established the objective function of the first-arrival traveltime tomography of the transmitted waves based on the MATV regularization and then used the split Bregman method to solve the objective function. The simulated data and real data show that the MATV regularization method proposed in this paper can better maintain the boundaries of geological anomalies and reduce the artifacts compared with the isotropic total variation regularization method and the anisotropic total variation regularization method. Furthermore, this approach describes the distribution of geological anomalies more accurately and effectively and improves imaging accuracy.

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
Sign in / Sign up

Export Citation Format

Share Document