Sparse-view image reconstruction via total absolute curvature combining total variation for X-ray computed tomography

2017 ◽  
Vol 25 (6) ◽  
pp. 959-980 ◽  
Author(s):  
Zhizhong Zheng ◽  
Ailong Cai ◽  
Lei Li ◽  
Bin Yan ◽  
Fulong Le ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
X Ray ◽  
Total Absolute Curvature ◽  
Absolute Curvature ◽  
X Ray Computed

scholarly journals A new Mumford–Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction

2018 ◽  
Vol 285 ◽  
pp. 74-81 ◽  
Author(s):  
Bo Chen ◽  
Zhaoying Bian ◽  
Xiaohui Zhou ◽  
Wensheng Chen ◽  
Jianhua Ma ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
Total Variation Minimization ◽  
X Ray ◽  
Tomography Image ◽  
X Ray Computed ◽  
Computed Tomography Image

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149899 ◽  
Author(s):  
Hanming Zhang ◽  
Linyuan Wang ◽  
Bin Yan ◽  
Lei Li ◽  
Ailong Cai ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
X Ray ◽  
Tomography Image ◽  
X Ray Computed ◽  
Computed Tomography Image

scholarly journals Low-dose X-ray computed tomography image reconstruction with a combined low-mAs and sparse-view protocol

2014 ◽  
Vol 22 (12) ◽  
pp. 15190 ◽  
Author(s):  
Yang Gao ◽  
Zhaoying Bian ◽  
Jing Huang ◽  
Yunwan Zhang ◽  
Shanzhou Niu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
X Ray ◽  
Tomography Image ◽  
X Ray Computed ◽  
Computed Tomography Image
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