A few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction

Journal of X-Ray Science and Technology

Volumn 21, Issue 2, 2013, Pages 161-176

  Chang, Ming ^a,b   Li, Liang ^a,b   Chen, Zhiqiang ^a,b   Xiao, Yongshun ^a,b   Zhang, Li ^a,b   Wang, Ge ^c  

^a TSINGHUA UNIVERSITY   (China)

^b KEY LABORATORY OF PARTICLE AND RADIATION IMAGING TSINGHUA UNIVERSITY   (China)

^c VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Few view; FISTA; reconstruction; reweighted TV; Split Bregman

Indexed keywords

CT IMAGE RECONSTRUCTION; EXPERIMENTAL EVALUATION; FEW-VIEW; FISTA; ITERATIVE SHRINKAGES; MINIMIZATION PROBLEMS; NUMERICAL EXPERIMENTS; SPLIT-BREGMAN;

ALGORITHMS; COMPUTERIZED TOMOGRAPHY; IMAGE RECONSTRUCTION;

ITERATIVE METHODS;

ALGORITHM; ANIMAL; BIOLOGICAL MODEL; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER SIMULATION; HEAD; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; PROCEDURES; RADIOGRAPHY; SHEEP; THORAX RADIOGRAPHY; ARTICLE; METHODOLOGY;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; HEAD; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, BIOLOGICAL; PHANTOMS, IMAGING; RADIOGRAPHY, THORACIC; SHEEP; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84880221633     PISSN: 08953996     EISSN: None     Source Type: Journal    
DOI: 10.3233/XST-130370     Document Type: Article

References (29)

1. E.J. Candes, J. Romberg and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory 52 (2006), 489-509
2. D.L. Donoho, Compressed sensing, IEEE Transactions on Information Theory 52 (2006), 1289-1306
3. I. Daubechies, M. Defrise and C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics 57 (2004), 1413-1457
4. E.J. Candes, J.K. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics 59 (2006), 1207-1223
5. P.T. Lauzier, J. Tang and G.H. Chen, Prior image constrained compressed sensing: Implementation and performance evaluation, Medical Physics 39 (2012), 66-80
6. H.Y. Yu and G. Wang, A soft-threshold filtering approach for reconstruction from a limited number of projections, Physics in Medicine and Biology 55 (2010), 3905-3916
7. H.Y. Yu and G. Wang, Compressed sensing based interior tomography, Physics in Medicine and Biology 54 (2009), 2791-2805
8. E.Y. Sidky and X.C. Pan, Image reconstruction in circular cone-beam computed tomography by constrained, totalvariation minimization, Physics in Medicine and Biology 53 (2008), 4777-4807
9. G.H. Chen, J. Tang and S.H. Leng, Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets, Medical Physics 35 (2008), 660-663
10. E.Y. Sidky, C.M. Kao and X.H. Pan, Accurate image reconstruction from few-views and limited-Angle data in divergentbeam CT, Journal of X-Ray Science and Technology 14 (2006), 119-139
11. X. Han, J. Bian, D.R. Eaker, T.L. Kline, E.Y. Sidky, E.L. Ritman and X. Pan, Algorithm-enabled low-dose micro-CT imaging, IEEE Transactions on Medical Imaging 30 (2011), 606-620
12. H. Yu and G. Wang, SART-type image reconstruction from a limited number of projections with the sparsity constraint, Journal of Biomedical Imaging 3 (2010), 1-9
13. J.C. Park, B.Y. Song, J.S. Kim, S.H. Park, H.K. Kim, Z.W. Liu, T.S. Suh and W.Y. Song, Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT, Medical Physics 39 (2012), 1207-1217
14. J. Barzilai and J.M. Borwein, 2-point step size gradient methods, IMA Journal of Numerical Analysis 8 (1988), 141-148
15. T. Goldstein and S. Osher, The split bregman method for L1-regularized problems, SIAM Journal on Imaging Sciences 2 (2009), 323-343
16. S. Ramani and J.A. Fessler, A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction, IEEE Transactions on Medical Imaging 31 (2012), 677-688
17. B. Vandeghinste, B. Goossens, J. De Beenhouwer, A. Pizurica, W. Philips, S. Vandenberghe and S. Staelens, Split- Bregman-based sparse-view CT reconstruction, Fully 3D 2011 Proceedings (2011), 431-434
18. X.Y. Tang et al., Enhancement of in-plane spatial resolution in volumetric computed tomography with focal spot wobbling-overcoming the constraint on number of projection views per gantry rotation, Journal of X-Ray Science and Technology 18(3) (2010), 251-265
19. L. Li et al., A curve-filtered FDK (C-FDK) reconstruction algorithm for circular cone-beam CT, Journal of X-Ray Science and Technology 19(3) (2011), 355-371
20. M. Fedrigo, A.Wenger and C. Hoeschen, Investigating tomographic reconstruction with a priori geometrical information, Journal of X-Ray Science and Technology 20(1) (2012), 1-10
21. Y. Lu et al., TV-based image reconstruction of multiple objects in a fixed source-detector geometry, Journal of X-Ray Science and Technology 20(3) (2012), 277-289
22. Z.T. Wang et al., Low dose reconstruction algorithm for differential phase contrast imaging, Journal of X-Ray Science and Technology 19(3) (2011), 403-415
23. X. Jia et al., GPU-based fast low-dose cone beam CT reconstruction via total variation, Journal of X-Ray Science and Technology 19(2) (2011), 139-154
24. J. Bian, J.H. Siewerdsen, X. Han, E.Y. Sidky, J.L. Prince, C.A. Pelizzari and X. Pan, Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT, Physics in Medicine and Biology 55 (2010), 6575-6599
25. J.H. Jørgensen, E.Y. Sidky and X. Pan, Analysis of discrete-to-discrete imaging models for iterative tomographic image reconstruction and compressive sensing, 2011, Available online: Http://arxiv.org/abs/1109.0629
26. E.J. Candes, M.B.Wakin and S.P. Boyd, Enhancing sparsity by reweighted l(1) minimization, Journal of Fourier Analysis and Applications 14 (2008), 877-905
27. A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences 2 (2009), 183-202
28. A. Beck and M. Teboulle, Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, IEEE Transactions on Image Processing 18 (2009), 2419-2434
29. Y. Nesterov, A method of solving a convex programming problem with convergence rate O(1/k2), Soviet Math 27(2) (1983), 372-376