Convex MR brain image reconstruction via non-convex total variation minimization

2018 ◽  
Vol 28 (4) ◽  
pp. 246-253 ◽  
Author(s):  
Yilin Liu ◽  
Huiqian Du ◽  
Zexian Wang ◽  
Wenbo Mei
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Total Variation Minimization ◽  
Brain Image ◽  
Mr Brain

A Compton scattering image reconstruction algorithm based on total variation minimization

2012 ◽  
Vol 21 (10) ◽  
pp. 108703 ◽  
Author(s):  
Shou-Peng Li ◽  
Lin-Yuan Wang ◽  
Bin Yan ◽  
Lei Li ◽  
Yong-Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compton Scattering ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Total Variation Minimization ◽  
Image Reconstruction Algorithm ◽  
Scattering Image

Image reconstruction from partial pseudo polar Fourier sampling based on alternating direction total variation minimization

2013 ◽  
Author(s):  
Qiu-hong Liu ◽  
Fan Shu ◽  
Wen-kun Zhang ◽  
Ai-long Cai ◽  
Lei Li ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Total Variation Minimization ◽  
Alternating Direction ◽  
Fourier Sampling

Gap compensation during PET image reconstruction by constrained, total variation minimization

2012 ◽  
Vol 39 (2) ◽  
pp. 589-602 ◽  
Author(s):  
Seonmin Ahn ◽  
Soo Mee Kim ◽  
Jungah Son ◽  
Dong Soo Lee ◽  
Jae Sung Lee
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Total Variation Minimization ◽  
Pet Image Reconstruction

Total variation minimization for in-beam PET image reconstruction

2012 ◽  
Author(s):  
Shoko Kinouchi ◽  
Taiga Yamaya ◽  
Hideaki Tashima ◽  
Eiji Yoshida ◽  
Hiroshi Ito ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Total Variation Minimization ◽  
Pet Image Reconstruction

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.

SU-E-I-38: Constrained - Total - Variation - Minimization - Based Image Reconstruction in Breast CT

2011 ◽  
Vol 38 (6Part4) ◽  
pp. 3404-3404
Author(s):  
J Bian ◽  
K Yang ◽  
X Han ◽  
E Sidky ◽  
J Boone ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Breast Ct ◽  
Total Variation Minimization

scholarly journals NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Yan ◽  
Zhao Jin ◽  
Hanming Zhang ◽  
Lei Li ◽  
Ailong Cai
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Imaging System ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Time Duration ◽  
Total Variation Minimization ◽  
Scanning Method ◽  
X Ray ◽  
Alternating Direction

Sparse-view imaging is a promising scanning method which can reduce the radiation dose in X-ray computed tomography (CT). Reconstruction algorithm for sparse-view imaging system is of significant importance. The adoption of the spatial iterative algorithm for CT image reconstruction has a low operation efficiency and high computation requirement. A novel Fourier-based iterative reconstruction technique that utilizes nonuniform fast Fourier transform is presented in this study along with the advanced total variation (TV) regularization for sparse-view CT. Combined with the alternating direction method, the proposed approach shows excellent efficiency and rapid convergence property. Numerical simulations and real data experiments are performed on a parallel beam CT. Experimental results validate that the proposed method has higher computational efficiency and better reconstruction quality than the conventional algorithms, such as simultaneous algebraic reconstruction technique using TV method and the alternating direction total variation minimization approach, with the same time duration. The proposed method appears to have extensive applications in X-ray CT imaging.

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