Joint-map tomographic reconstruction with patch similarity based mixture prior model

Multiscale Modeling and Simulation

Volumn 9, Issue 4, 2011, Pages 1399-1419

  Chen, Yang ^a,b,c,d   Li, Yinsheng ^a   Yu, Weimin ^a   Luo, Limin ^a,d   Chen, Wufan ^e   Toumoulin, Christine ^b,c,d  

^a SOUTHEAST UNIVERSITY   (China)

^b INSERM   (France)

^c UNIVERSITÉ DE RENNES 1   (France)

^d Centre de Recherche en Information Biomedicale Sino Francais LIA CRIBs   (France)

^e SOUTHERN MEDICAL UNIVERSITY   (China)

Author keywords

Etc.; Image analysis; Image processing compression; Inverse problems; Parallel computation; Random field; Reconstruction

Indexed keywords

ANALYTICAL METHOD; BAYESIAN RECONSTRUCTION; COMPUTE UNIFIED DEVICE ARCHITECTURES; COMPUTED TOMOGRAPHY; COMPUTED TOMOGRAPHY DATA; ETC.; FILTERED BACK-PROJECTION; GAUSSIANS; HIGH QUALITY IMAGES; ILL POSED PROBLEM; JOINT ALGORITHMS; MAXIMUM A POSTERIORI; MIXTURE PRIORS; NON-LOCAL PRIORS; PARALLEL COMPUTATION; PARALLELIZATIONS; PATCH DISTANCE; RANDOM FIELDS; RECONSTRUCTION ALGORITHMS; ROBUST UTILIZATION; TOMOGRAPHIC RECONSTRUCTION; WEIGHT UPDATE;

ALGORITHMS; COMPUTERIZED TOMOGRAPHY; IMAGE ANALYSIS; IMAGE QUALITY; IMAGE RECONSTRUCTION; INVERSE PROBLEMS; TOMOGRAPHY;

HEURISTIC METHODS;

EID: 84863011094     PISSN: 15403459     EISSN: 15403467     Source Type: Journal    
DOI: 10.1137/100814184     Document Type: Article

References (34)

1. A. C. KAT AND M. STANEY, Principles of Computerized Tomographic imaging, IEEE Press, New York, 1988, pp. 177-201.
2. C. ZHANG, C. LI, AND L. V. WANG, Fast and robust deconvolution-based image reconstruction for photoacoustic tomography in circular geometry: Experimental validation, IEEE Photonics J., 2 (2010), pp. 57-66.
3. F. J. BEEKMAN AND C. KAMPHUIS, Ordered subset reconstruction for x-ray CT, Phys. Med. Biol., 46 (2001), pp. 1835-1844. (Pubitemid 32677542)
4. I. A. ELBAKRI AND J. A. FESSLER, Efficient and accurate likelihood for iterative image reconstruction in X-ray computed tomography, in Proceedings of SPIE, Medical Imaging 2003, 5032, 2003, pp. 1839-1850.
5. L. A. SHEPP AND Y. VARDI, Maximum likelihood reconstruction for emission tomography, IEEE Trans. Med. Imaging, 1 (1982), pp. 113-122. (Pubitemid 13557763)
6. K. LANGE AND J. A. FESSLER, Globally convergent algorithms for maximum a posteriori transmission tomography, IEEE Trans. Image Process., 4 (1995), pp. 1430-1438.
7. M. BERTERO, T. POGGIO, AND V. TORRE, Ill posed problems in early vision, Proc. IEEE, 76 (1988), pp. 869-889.
8. K. LANGE, Convergence of EM image reconstruction algorithms with Gibbs smoothness, IEEE Trans. Med. Imaging, 9 (1990), pp. 439-446.
9. H. ERDOGAN AND J. A. FESSLER, Monotonic algorithms for transmission tomography, IEEE Trans. Med. Imaging, 18 (1999), pp. 801-814. (Pubitemid 30519199)
10. W. CHLEWICKI, F. HERMANSEN, AND S. B. HANSEN, Noise reduction and convergence of Bayesian algorithms with blobs based on the Huber function and median root prior, Phys. Med. Biol., 49 (2004), pp. 4717-4730. (Pubitemid 39433501)
11. C. A BOUMAN AND K. SAUER, A generalized Gaussian image model for edge-preserving MAP estimation, IEEE Trans. Image Process., 2 (1993), pp. 296-310. (Pubitemid 23697158)
12. V. DUVAL, J. F. AUJOL, AND Y. GOUSSEAU, The TVL1 model: A geometric point of view, Multiscale Model. Simul., 8 (2009), pp. 154-189.
13. Y. CHEN, D.GAO, C.NIE, L. LUO,W. CHEN, X. YIN, AND Y. LIN, Bayesian statistical reconstruction for lowdose X-ray computed tomography using an adaptive-weighting nonlocal prior, Computerized Medical Imaging and Graph, 33 (2009), pp. 495-500.
14. D. F. YU AND J. A. FESSLER, Edge-preserving tomographic reconstruction with nonlocal regularization, IEEE Trans. Med. Imaging, 21 (2002), pp. 159-173. (Pubitemid 34466429)
15. S. Z. LI, Markov Random Field Modeling in Image Analysis, Springer-Verlag, Tokyo, 2001.
16. A. BUADES, B.COLL, AND J. M.MOREL, A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 4 (2005), pp. 490-530. (Pubitemid 43890582)
17. Y. CHEN, J. H. MA, Q. FENG, L.M. LUO, P. C. SHI, AND W. F. CHEN, Nonlocal prior Bayesian tomographic reconstruction, J. Math. Imaging Vision, 30 (2008), pp. 133-146.
18. NVIDIA CUDA Programming Guide (Version 3.0), http://developer.download. nvidia.com/compute/ cuda/30/toolkit/docs/NVIDIA-CUDA-ProgrammingGuide.pdf.
19. Accelerating MATLAB with CUDA Using MEX Files (White Paper), http://developer.nvidia.com/ object/matlab-cuda.html.
20. GPU Acceleration in MATLAB, http://arch.eece.maine.edu/superme/images/8/ 8c/Final-report.pdf.
21. S. W. LYU, An implicit Markov random field model for the multi-scale oriented representations of natural images, in IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1919-1925.
22. P.WINKLER, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, 2nd ed., Springer, Berlin, 2003.
23. G. PEYR, S. BOUGLEUX, AND L. COHEN, Non-local regularization of inverse problems, in ECCV '08 Proceedings of the 10th European Conference on Computer Vision, Springer-Verlag, Berlin, Heidelberg, 2008, pp. 57-68.
24. G. GILBOA AND S. OSHER, Nonlocal operators with applications to image processing, Multiscale Model. Simul., 7 (2008), pp. 1005-1028.
25. S. C. ZHU, Y. N.WU, AND D. B.MUMFORD, FRAME: Filters, random field and maximum entropy:Towards a unified theory for texture modeling, Int. J. Comput. Vis., 27 (1998) pp. 1-20.
26. I. T. HSIAO, A. RANGARAJAN, AND G. R. GINDI, Bayesian image reconstruction for transmission tomography using deterministic annealing, J. Electron. Imaging, 12 (2003), pp. 7-16.
27. C. A. BOUMAN AND K. SAUER, A unified approach to statistical tomography using coordinate descent optimization, IEEE Trans. Image Process., 5 (1996), pp. 480-492. (Pubitemid 126778108)
28. F. L. HUANG, C. J HSIEH, K.W. CHANG, AND C. J. LIN, Iterative scaling and coordinate descent methods for maximum entropy models, J. Mach. Learn. Res., 11 (2010), pp. 815-848.
29. K. LANGE, D. R. HUNTER, AND I. YANG, Optimization transfer using surrogate objective functions, J. Comput. Graph. Statist., 9 (2000), pp. 1-20.
30. C. A. DELEDALLE, L. DENIS, AND F. TUPIN, Iterative weighted maximum likelihood denoising with probabilistic patch-based weights, IEEE Trans. Image Process., 18 (2009), pp. 2661-2672.
31. W. ZBIJEWSKI AND F. J. BEEKMAN, Suppression of intensity transition artifacts in statistical X-ray computer tomography through radon inversion initialization, Med. Phys., 31 (2004), pp. 62-69. (Pubitemid 38113867)
32. J. A. FESSLER, Aspire 3.0 User's Guide: A Sparse Reconstruction Library, Communication & Signal Processing Laboratory Technical report No. 293, Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, 1998.
33. H. SCHERL, B. KECK, M. KOWARSCHIK, AND J. HORNEGGER, Fast GPU-based CT reconstruction using the common unified device architecture (CUDA), in Proceedings of the 2007 IEEE Nuclear Science Symposium Conference, Honolulu, HI, 2007, pp. 4464-4466.
34. M. KNAUP, S. STECKMANN, AND M. KACHELRIE, GPU-based parallel-beam and cone-beam forward-and backprojection using CUDA, in Proceedings of the 2008 IEEE Nuclear Science Symposium Conference, Dresden, Germany, 2008, pp. 5153-5157.