Optimization-based image reconstruction in computed tomography by alternating direction method with ordered subsets

2017 ◽  
Vol 25 (3) ◽  
pp. 429-464 ◽  
Author(s):  
Ailong Cai ◽  
Linyuan Wang ◽  
Lei Li ◽  
Bin Yan ◽  
Zhizhong Zheng ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Alternating Direction Method ◽  
Alternating Direction

scholarly journals Distributed Reconstruction via Alternating Direction Method

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Linyuan Wang ◽  
Ailong Cai ◽  
Hanming Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Considerable Research ◽  
Alternating Direction ◽  
Ct Image Reconstruction

With the development of compressive sensing theory, image reconstruction from few-view projections has received considerable research attentions in the field of computed tomography (CT). Total-variation- (TV-) based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, a distributed reconstruction algorithm based on TV minimization has been developed. This algorithm is very simple as it uses the alternating direction method. The proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm without losing accuracy.

scholarly journals An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xiezhang Li ◽  
Guocan Feng ◽  
Jiehua Zhu
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Numerical Experiments ◽  
Projection Data ◽  
Hard Thresholding ◽  
L1 Norm ◽  
Regularization Algorithm ◽  
Alternating Direction ◽  
Algebraic Framework ◽  
Ct Image Reconstruction

The l1-norm regularization has attracted attention for image reconstruction in computed tomography. The l0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l1-norm and l0-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l0-norm regularization.

Inexact Distributed Reconstruction via Alternating Direction Method

2014 ◽  
Vol 511-512 ◽  
pp. 417-420
Author(s):  
Lin Yuan Wang ◽  
Ai Long Cai ◽  
Bin Yan ◽  
Lei Li ◽  
Han Ming Zhang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Experimental Results ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Alternating Direction ◽  
Proximal Points ◽  
Ct Image Reconstruction

Total variation (TV)-based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, an inexact distributed reconstruction algorithm based on TV minimization has been developed. The algorithm is relatively simple as it uses the inexact alternating direction method, which involves linearization and proximal points techniques. The outstanding acceleration factor is achieved as the algorithm distributes the data and computation to individual nodes. Experimental results demonstrate that the proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm with very little accuracy loss.

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