Low-dose X-ray CT reconstruction via dictionary learning

IEEE Transactions on Medical Imaging

Volumn 31, Issue 9, 2012, Pages 1682-1697

  Xu, Qiong ^a,b   Yu, Heng Yong ^b   Mou, Xuan Qin ^a   Zhang, Lei ^c   Hsieh, Jiang ^d   Wang, Ge ^b,e  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b Virginia Tech Wake Forest School of Biomedical Engineering and Sciences   (United States)

^c HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^d GE HEALTHCARE   (United States)

^e WAKE FOREST UNIVERSITY HEALTH SCIENCES   (United States)

Author keywords

Compressive sensing (CS); Computed tomography (CT); Dictionary learning; Low dose CT; Sparse representation; Statistical iterative reconstruction

Indexed keywords

COMPRESSIVE SENSING; COMPUTED TOMOGRAPHY; DICTIONARY LEARNING; LOW-DOSE CT; SPARSE REPRESENTATION; STATISTICAL ITERATIVE RECONSTRUCTION;

CONSTRAINT THEORY; DIAGNOSIS; IMAGE RECONSTRUCTION; ITERATIVE METHODS; MEDICAL IMAGING; X RAYS;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER SIMULATION; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; INSTRUMENTATION; LUNG; METHODOLOGY; RADIOGRAPHY; SHEEP; THORAX RADIOGRAPHY;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG; PHANTOMS, IMAGING; RADIOGRAPHY, THORACIC; SHEEP; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84867561335     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2012.2195669     Document Type: Article

References (50)

1. D. J. Brenner, C. D. Elliston, E. J. Hall, and W. E. Berdon, "Estimated risks of radiation-induced fatal cancer from pediatric CT," Am. J. Roentgenol., vol. 176, pp. 289-296, 2001.
2. A. B. de Gonzalez and S. Darby, "Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries," Lancet, vol. 363, pp. 345-351, 2004.
3. D. J. Brenner and E. J. Hall, "Computed tomography-An increasing source of radiation exposure," New Eng. J. Med., vol. 357, pp. 2277-2284, 2007.
4. J. Hsieh, "Adaptive streak artifact reduction in computed tomography resulting from excessive X-ray photon noise," Med. Phys., vol. 25, pp. 2139-2147, 1998.
5. M. Kachelriess, O. Watzke, and W. A. Kalender, "Generalized multidimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT," Med. Phys., vol. 28, pp. 475-490, 2001.
6. P. La Riviire, "Penalized-likelihood sinogram smoothing for low-dose CT," Med. Phys., vol. 32, p. 1676, 2005.
7. J.Wang, T. Li, H. Lu, and Z. Liang, "Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography," IEEE Trans. Med. Imag., vol. 25, no. 10, pp. 1272-1283, Oct. 2006.
8. C. Kamphuis and F. Beekman, "Accelerated iterative transmission CT reconstruction using an ordered subsets convex algorithm," IEEE Trans. Med. Imag., vol. 17, no. 6, pp. 1101-1105, Dec. 1998.
9. J. Nuyts, B. De Man, P. Dupont, M. Defrise, P. Suetens, and L.Mortelmans, "Iterative reconstruction for helical CT: A simulation study," Phys. Med. Biol., vol. 43, pp. 729-737, 1998.
10. H. Erdogan and J. Fessler, "Monotonic algorithms for transmission tomography," IEEE Trans. Med. Imag., vol. 18, no. 9, pp. 801-814, Sep. 1999.
11. J. Thibault, K. Sauer, C. Bouman, and J. Hsieh, "A three-dimensional statistical approach to improved image quality for multislice helical CT," Med. Phys., vol. 34, p. 4526, 2007.
12. D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, pp. 1289-1306, 2006.
13. E. J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
14. G. H. Chen, J. Tang, and S. Leng, "Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets," Med. Phys., vol. 35, p. 660, 2008.
15. H. Y. Yu and G. Wang, "Compressed sensing based interior tomography," Phys. Med. Biol., vol. 54, pp. 2791-2805, 2009.
16. H. Y. Yu, J. S. Yang, M. Jiang, and G. Wang, "Supplemental analysis on compressed sensing based interior tomography," Phys. Med. Biol., vol. 54, pp. N425-N432, 2009.
17. Q. Xu, X. Mou, G. Wang, J. Sieren, E. Hoffman, and H. Yu, "Statistical interior tomography," IEEE Trans. Med. Imag., vol. 30, no. 5, pp. 1116-1128, May 2011.
18. L. Ritschl, F. Bergner, C. Fleischmann, and M. Kachelriei, "Improved total variation-based CT image reconstruction applied to clinical data," Phys. Med. Biol., vol. 56, p. 1545, 2011.
19. D. L. Donoho and M. Elad, "Maximal sparsity representation via l1 minimization," Proc. Nat. Aca. Sci., vol. 100, pp. 2197-2202, 2003.
20. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, Dec. 2006.
21. J. Mairal, M. Elad, and G. Sapiro, "Sparse representation for color image restoration," IEEE Trans. Image Process., vol. 17, no. 1, pp. 53-69, Jan. 2007.
22. J. Mairal, G. Sapiro, and M. Elad, "Learning multiscale sparse representations for image and video restoration," SIAM Multiscale Model. Simulat., vol. 7, pp. 214-241, 2008.
23. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2008.
24. Y. Chen, X. Ye, and F. Huang, "A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data," Inverse Problems Imag., vol. 4, pp. 223-240, 2010.
25. S. Ravishankar and Y. Bresler, "MR image reconstruction from highly undersampled -space data by dictionary learning," IEEE Trans. Med. Imag., vol. 30, no. 5, pp. 1028-1041, May 2011.
26. Q. Xu, H. Yu, X. Mou, and G. Wang, "Dictionary learning based lowdose X-ray CT reconstruction," in Proc. 11th Int.Meeting Fully Three- Dimensional Image Reconstruct. Radiol. Nucl. Med., Potsdam, Germany, 2011, pp. 258-261.
27. S. G. Mallat and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. Signal Process., vol. 41, pp. 3397-3415, 1993.
28. S. Chen, S. Billings, and W. Luo, "Orthogonal least squares methods and their application to non-linear system identification," Int. J. Control, vol. 50, pp. 1873-1896, 1989.
29. Y. C. Pati, R. Rezaiifar, and P. Krishnaprasad, "Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition," in Proc. 27th Asilomar Conf. Signals Syst. Comput., 1993, vol. 1, pp. 40-44.
30. S. S. Chen, D. L. Donoho, and M.A. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Sci. Comput., vol. 20, pp. 33-61, 1999.
31. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
32. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online learning for matrix factorization and sparse coding," J. Mach. Learn. Res., vol. 11, pp. 19-60, 2010.
33. K. Sauer and C. Bouman, "A local update strategy for iterative reconstruction from projections," IEEE Trans. Signal Process., vol. 41, no. 2, pp. 534-548, Feb. 1993.
34. I. A. Elbakri and J. A. Fessler, "Statistical image reconstruction for polyenergetic X-ray computed tomography," IEEE Trans. Med. Imag., vol. 21, no. 2, pp. 89-99, Feb. 2002.
35. T. Li, X. Li, J.Wang, J. Wen, H. Lu, J.Hsieh, and Z. Liang, "Nonlinear sinogram smoothing for low-dose X-ray CT," IEEE Trans. Nucl. Sci., vol. 51, no. 5, pp. 2505-2513, Oct. 2004.
36. K. Little, P. Vargas, and P. L. Riviere, "Weight choice in PWLS algorithms for emission and transmission tomography," in IEEE Nucl. Sci. Symp. Conf. Rec., 2010, vol. M17-3.
37. B. R. Whiting, P. Massoumzadeh, O. A. Earl, J. A. O'Sullivan, D. L. Snyder, and J. F. Williamson, "Properties of preprocessed sinogram data in X-ray computed tomography," Med. Phys., vol. 33, p. 3290, 2006.
38. J. B. Thibault, K. D. Sauer, C. A. Bouman, and J. Hsieh, "A three-dimensional statistical approach to improved image quality formultislice helical CT," Med. Phys., vol. 34, p. 4526, 2007.
39. C. Bouman and K. Sauer, "A generalized Gaussian image model for edge-preserving MAP estimation," IEEE Trans. Image Process., vol. 2, no. 3, pp. 296-310, Jul. 1993.
40. [Online]. Available: http://www.di.ens.fr/willow/SPAMS/
41. M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging. Oxford, U.K.: Taylor Francis, 1998, ch. 5.6.
42. K. Miller, "Least squares methods for ill-posed problems with a prescribed bound," SIAM J. Math. Anal., vol. 1, p. 52, 1970.
43. G. H. Golub, M. Heath, and G. Wahba, "Generalized cross-validation as a method for choosing a good ridge parameter," Technometrics, vol. 21, pp. 215-223, 1979.
44. J. Mairal, G. Sapiro, and M. Elad, "Learning multiscale sparse representations for image and video restoration," SIAM Multiscale Model. Simulat., vol. 7, pp. 214-241, 2008.
45. H. Y. Yu and G. Wang, "A soft-threshold filtering approach for reconstruction from a limited number of projections," Phys. Med. Biol., vol. 55, pp. 3905-3916, 2010.
46. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.
47. [Online]. Available: http://www.radiolog.at/start.php?bereich=ct- mr&nav=untersuchungen&lw=computertomographie
48. J. Song, Q. H. Liu, G. A. Johnson, and C. T. Badea, "Sparseness prior based iterative image reconstruction for retrospectively gated cardiac micro-CT," Med. Phys., vol. 34, pp. 4476-4483, 2007.
49. H. Y. Yu and G.Wang, "Finite detector based projection model for high spatial resolution," J. X-Ray Sci. Technol., vol. 20, no. 2, pp. 229-238, 2012.
50. B. De Man and S. Basu, "Distance-driven projection and backprojection in three dimensions," Phys. Med. Biol., vol. 49, pp. 2463-2475, 2004.