scholarly journals Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yi Zhang ◽  
Weihua Zhang ◽  
Jiliu Zhou
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Clinical Results ◽  
Low Rank ◽  
Projection Data ◽  
Total Variation Norm ◽  
Structure Information ◽  
Nonlocal Total Variation ◽  
Projection Image ◽  
Sparse Projection

Sparse-projection image reconstruction is a useful approach to lower the radiation dose; however, the incompleteness of projection data will cause degeneration of imaging quality. As a typical compressive sensing method, total variation has obtained great attention on this problem. Suffering from the theoretical imperfection, total variation will produce blocky effect on smooth regions and blur edges. To overcome this problem, in this paper, we introduce the nonlocal total variation into sparse-projection image reconstruction and formulate the minimization problem with new nonlocal total variation norm. The qualitative and quantitative analyses of numerical as well as clinical results demonstrate the validity of the proposed method. Comparing to other existing methods, our method more efficiently suppresses artifacts caused by low-rank reconstruction and reserves structure information better.

scholarly journals CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hongliang Qi ◽  
Zijia Chen ◽  
Linghong Zhou
Keyword(s):  
Image Reconstruction ◽  
Ct Image ◽  
Minimization Method ◽  
X Ray ◽  
Structure Information ◽  
Reconstruction Methods ◽  
Projection Image ◽  
Sparse Projection ◽  
Ct Image Quality ◽  
Better Than

Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel adaptive TpV regularization into sparse-projection image reconstruction and use FISTA technique to accelerate iterative convergence. The numerical experiments demonstrate that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods.

scholarly journals Hyperspectral Image Reconstruction via Block Low-Rank and Three-Dimension Weighted Total Variation Constraint

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47698-47713 ◽  
Author(s):  
Zongrui Wu ◽  
Xi Chen ◽  
Wenxuan Shi ◽  
Liqiong Chen ◽  
Shiyong Hu
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Three Dimension ◽  
Weighted Total Variation

scholarly journals MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Varun P. Gopi ◽  
P. Palanisamy ◽  
Khan A. Wahid ◽  
Paul Babyn
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Least Square ◽  
Split Bregman ◽  
Mr Image ◽  
Split Bregman Iteration ◽  
Nonlocal Total Variation ◽  
Comparison Results ◽  
Space Data ◽  
Mr Image Reconstruction

This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampledk-space data. The nonlocal total variation is taken as theL1-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.

scholarly journals Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Chengzhi Deng ◽  
Shengqian Wang ◽  
Wei Tian ◽  
Zhaoming Wu ◽  
Saifeng Hu
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Optimization Problems ◽  
Reconstruction Method ◽  
Mr Image ◽  
Scanning Time ◽  
Reconstruction Methods ◽  
Nonlocal Total Variation ◽  
Space Data ◽  
Mr Image Reconstruction

Recent developments in compressive sensing (CS) show that it is possible to accurately reconstruct the magnetic resonance (MR) image from undersampledk-space data by solving nonsmooth convex optimization problems, which therefore significantly reduce the scanning time. In this paper, we propose a new MR image reconstruction method based on a compound regularization model associated with the nonlocal total variation (NLTV) and the wavelet approximate sparsity. Nonlocal total variation can restore periodic textures and local geometric information better than total variation. The wavelet approximate sparsity achieves more accurate sparse reconstruction than fixed waveletl0andl1norm. Furthermore, a variable splitting and augmented Lagrangian algorithm is presented to solve the proposed minimization problem. Experimental results on MR image reconstruction demonstrate that the proposed method outperforms many existing MR image reconstruction methods both in quantitative and in visual quality assessment.

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