Characterization of statistical prior image constrained compressed sensing. I. Applications to time-resolved contrast-enhanced CT

Medical Physics

Volumn 39, Issue 10, 2012, Pages 5930-5948

  Lauzier, Pascal Thériault ^a   Chen, Guang Hong ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

compressed sensing; computed tomography; dynamic imaging; iterative reconstruction algorithms; maximum a posteriori estimation

Indexed keywords

COMPRESSED SENSING; IMAGE COMPRESSION; IMAGE ENHANCEMENT; IMAGE QUALITY; IMAGE RECONSTRUCTION; ITERATIVE METHODS; NUMERICAL METHODS; POWER SPECTRUM; STATISTICS;

DYNAMIC IMAGING; FILTERED BACK-PROJECTION; INSTITUTIONAL REVIEW BOARDS; ITERATIVE RECONSTRUCTION ALGORITHMS; MAXIMUM A POSTERIORI ESTIMATION; QUANTITATIVE METRICS; RECONSTRUCTION FRAMEWORKS; STATISTICAL FORMULATION;

COMPUTERIZED TOMOGRAPHY;

EID: 84867290315     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4748323     Document Type: Article

References (66)

1. G. 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. 35, 660-663 (2008). 10.1118/1.2836423
2. S. Leng, J. Tang, J. Zambelli, B. Nett, R. Tolakanahalli, and G. Chen, High temporal resolution and streak-free four-dimensional cone-beam computed tomography., Phys. Med. Biol. 53, 5653-5673 (2008). 10.1088/0031-9155/53/20/006
3. G. Chen, J. Tang, and J. Hsieh, Temporal resolution improvement using PICCS in MDCT cardiac imaging., Med. Phys. 36, 2130-2135 (2009). 10.1118/1.3130018
4. J. Tang, J. Hsieh, and G. Chen, Temporal resolution improvement in cardiac CT using PICCS (TRI-PICCS): Performance studies., Med. Phys. 37, 4377-4388 (2010). 10.1118/1.3460318
5. T. Szczykutowicz and G. Chen, Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing., Phys. Med. Biol. 55, 6411-6429 (2010). 10.1088/0031-9155/55/21/005
6. B. Nett, R. Brauweiler, W. Kalender, H. Rowley, and G. Chen, Perfusion measurements by micro-CT using prior image constrained compressed sensing (PICCS): Initial phantom results., Phys. Med. Biol. 55, 2333-2349 (2010). 10.1088/0031-9155/55/8/014
7. J. Ramirez-Giraldo, J. Trzasko, S. Leng, C. McCollough, and A. Manduca, Non-convex prior image constrained compressed sensing (NC-PICCS)., Proc. SPIE 7622, 76222C-76222C-8 (2010). 10.1117/12.837239
8. J. Tang, P. Thériault Lauzier, and G. Chen, Dose reduction using prior image constrained compressed sensing (DR-PICCS)., Proc. SPIE 7961, 79612K-79612K-8 (2011). 10.1117/12.878200
9. M. Lubner, P. Pickhardt, J. Tang, and G. Chen, Reduced image noise at low-dose multidetector ct of the abdomen with prior image constrained compressed sensing algorithm., Radiology 260, 248-256 (2011).
10. Z. Qi and G.-H. Chen, Extraction of tumor motion trajectories using prior image constrained compressed sensing based four-dimensional Cone Beam CT (PICCS-4DCBCT): A validation study., Med. Phys. 38, 5530-5538 (2011). 10.1118/1.3637501
11. Z. Qi and G.-H. Chen, Performance studies of PICCS based four-dimensional cone beam computed tomography (PICCS-4DCBCT)., Phys. Med. Biol. 56, 6709-6721 (2011). 10.1088/0031-9155/56/20/013
12. J. Ramirez-Giraldo, J. Trzasko, S. Leng, L. Yu, A. Manduca, and C. McCollough, Nonconvex prior image constrained compressed sensing (NC-PICCS): Theory and simulations on perfusion ct., Med. Phys. 38, 2157-2167 (2011). 10.1118/1.3560878
13. G. Chen, P. Thériault Lauzier, J. Tang, B. Nett, S. Leng, J. Zambelli, Z. Qi, N. Bevins, A. Raval, and H. Rowley, Time-resolved interventional cardiac C-arm cone-beam CT: An application of the PICCS algorithm., IEEE Trans. Med. Imaging 31, 907-923 (2012). 10.1109/TMI.2011. 2172951
14. P. T. Lauzier, J. Tang, and G.-H. Chen, Prior image constrained compressed sensing: Implementation and performance evaluation., Med. Phys. 39, 66-80 (2012). 10.1118/1.3666946
15. P. T. Lauzier, J. Tang, and G.-H. Chen, Time-resolved cardiac interventional cone-beam CT reconstruction from fully truncated projections using the prior image constrained compressed sensing (PICCS) algorithm., Phys. Med. Biol. 57, 2461-2476 (2012). 10.1088/0031-9155/57/9/2461
16. J. Stayman, W. Zbijewski, Y. Otake, A. Uneri, S. Schafer, J. Lee, J. Prince, and J. Siewerdsen, Penalized-likelihood reconstruction for sparse data acquisitions with unregistered prior images and compressed sensing penalties., Proc. SPIE 7961, 79611L-79611L-6 (2011). 10.1117/12.878075
17. R. Kashyap and M. Mittal, Picture reconstruction from projections., IEEE Trans. Comput. C-24, 915-923 (1975). 10.1109/T-C.1975.224337
18. A. J. Rockmore and A. Macovski, A maximum likelihood approach to transmission image reconstruction from projections., Nuclear Science, IEEE Trans. Nucl. Sci. 24, 1929-1935 (1977). 10.1109/TNS.1977.4329128
19. K. Lange and R. Carson, EM reconstruction algorithms for emission and transmission tomography., J. Comput. Assist. Tomogr. 8, 306-316 (1984).
20. K. Sauer and C. Bouman, A local update strategy for iterative reconstruction from projections., IEEE Trans. Signal Process. 41, 534-548 (1993). 10.1109/78.193196
21. S. Manglos, G. Gagne, A. Krol, F. Thomas, and R. Narayanaswamy, Transmission maximum-likelihood reconstruction with ordered subsets for cone beam CT., Phys. Med. Biol. 40, 1225-1241 (1995). 10.1088/0031-9155/40/7/006
22. C. Bouman and K. Sauer, A unified approach to statistical tomography using coordinate descent optimization., IEEE Trans. Image Process. 5, 480-492 (1996). 10.1109/83.491321
23. C. Kamphuis and F. Beekman, Accelerated iterative transmission ct reconstruction using an ordered subsets convex algorithm., IEEE Trans. Med. Imaging 17, 1101-1105 (1998). 10.1109/42.746730
24. H. Erdogan and J. Fessler, Ordered subsets algorithms for transmission tomography., Phys. Med. Biol. 44, 2835-2851 (1999). 10.1088/0031-9155/44/11/311
25. J. Thibault, K. Sauer, C. Bouman, and J. Hsieh, A three-dimensional statistical approach to improved image quality for multislice helical CT., Med. Phys. 34, 4526-4544 (2007). 10.1118/1.2789499
26. L. Axel, Cerebral blood flow determination by rapid-sequence computed tomography: Theoretical analysis., Radiology 137, 679-686 (1980).
27. M. Koenig, E. Klotz, B. Luka, D. Venderink, J. Spittler, and L. Heuser, Perfusion CT of the brain: Diagnostic approach for early detection of ischemic stroke., Radiology 209, 85-93 (1998).
28. E. Klotz and M. Konig, Perfusion measurements of the brain: Using dynamic CT for the quantitative assessment of cerebral ischemia in acute stroke., Eur. J. Radiol. 30, 170-184 (1999). 10.1016/S0720-048X(99)00009-1
29. D. Nabavi, A. Cenic, R. Craen, A. Gelb, J. Bennett, R. Kozak, and T. Lee, CT assessment of cerebral perfusion: Experimental validation and initial clinical experience., Radiology 213, 141-149 (1999).
30. J. Rumberger, A. Feiring, M. Lipton, C. Higgins, S. Ell, and M. Marcus, Use of ultrafast computed tomography to quantitate regional myocardial perfusion: A preliminary report., J. Am. Coll. Cardiol. 9, 59-69 (1987). 10.1016/S0735-1097(87)80083-9
31. C. Wolfkiel, J. Ferguson, E. Chomka, W. Law, I. Labin, M. Tenzer, M. Booker, and B. Brundage, Measurement of myocardial blood flow by ultrafast computed tomography., Circulation 76, 1262-1273 (1987). 10.1161/01.CIR.76.6.1262
32. M. Wintermark, W. Smith, N. Ko, M. Quist, P. Schnyder, and W. Dillon, Dynamic perfusion CT: optimizing the temporal resolution and contrast volume for calculation of perfusion ct parameters in stroke patients., Am. J. Neuroradiol. 25, 720-729 (2004).
33. A. Kämena, F. Streitparth, C. Grieser, L. Lehmkuhl, B. Jamil, K. Wojtal, J. Ricke, and M. Pech, Dynamic perfusion CT: optimizing the temporal resolution for the calculation of perfusion ct parameters in stroke patients., Eur. J. Radiol. 64, 111-118 (2007). 10.1016/j.ejrad.2007.02.025
34. M. Supanich, Y. Tao, B. Nett, K. Pulfer, J. Hsieh, P. Turski, C. Mistretta, H. Rowley, and G. Chen, Radiation dose reduction in time-resolved CT angiography using highly constrained back projection reconstruction., Phys. Med. Biol. 54, 4575-4593 (2009). 10.1088/0031-9155/54/14/013
35. X. Liu, A. Primak, J. Krier, L. Yu, L. Lerman, and C. McCollough, Renal perfusion and hemodynamics: Accurate in vivo determination at CT with a 10-fold decrease in radiation dose and hyper noise reduction1., Radiology 253, 98-105 (2009). 10.1148/radiol.2531081677
36. J. Bushberg, The Essential Physics of Medical Imaging (Williams and Wilkins, 2002).
37. B. R. Whiting, Signal statistics in x-ray computed tomography., Proc. SPIE 4682, 53-60 (2002). 10.1117/12.465601
38. J. Tang, B. Nett, and G. Chen, Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms., Phys. Med. Biol. 54, 5781-5804 (2009). 10.1088/0031-9155/54/19/008
39. J. A. Fessler, Statistical Reconstruction in CT., SPIE Medical Imaging Workshop (2011); see: http://web.eecs.umich.edu/~fessler/papers/files/talk/11/ spie.pdf.
40. S. Ramani and J. Fessler, A splitting-based iterative algorithm for accelerated statistical x-ray CT reconstruction., IEEE Trans. Med. Imaging 31, 677-688 (2012).
41. E. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information., IEEE Trans. Inf. Theory 52, 489-509 (2006). 10.1109/TIT.2005.862083
42. E. Candès, J. Romberg, and T. Tao, Stable signal recovery from incomplete and inaccurate measurements., Commun. Pure Appl. Math. 59, 1207-1223 (2006). 10.1002/cpa.20124
43. D. Donoho, Compressed sensing., IEEE Trans. Inf. Theory 52, 1289-1306 (2006). 10.1109/TIT.2006.871582
44. L. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms., Physica D 60, 259-268 (1992). 10.1016/0167-2789(92)90242-F
45. J. Song, Q. Liu, G. Johnson, and C. Badea, Sparseness prior based iterative image reconstruction for retrospectively gated cardiac micro-CT., Med. Phys. 34, 4476-4483 (2007). 10.1118/1.2795830
46. E. Sidky and X. Pan, Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization., Phys. Med. Biol. 53, 4777-4807 (2008). 10.1088/0031-9155/53/17/021
47. E. Sidky, X. Pan, I. Reiser, R. Nishikawa, R. Moore, and D. Kopans, Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms., Med. Phys. 36, 4920-4932 (2009). 10.1118/1.3232211
48. K. Choi, J. Wang, L. Zhu, T. Suh, S. Boyd, and L. Xing, Compressed sensing based cone-beam computed tomography reconstruction with a first-order method., Med. Phys. 37, 5113-5125 (2010). 10.1118/1.3481510
49. J. Bian, J. Siewerdsen, X. Han, E. Sidky, J. Prince, C. Pelizzari, and X. Pan, Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT., Phys. Med. Biol. 55, 6575-6599 (2010). 10.1088/0031-9155/55/22/ 001
50. L. Ritschl, F. Bergner, C. Fleischmann, and M. Kachelrie, Improved total variation-based CT image reconstruction applied to clinical data., Phys. Med. Biol. 56, 1545-1561 (2011). 10.1088/0031-9155/56/6/003
51. J. Nocedal and S. Wright, Numerical Optimization (Springer, 1999).
52. R. Wagner, D. Brown, and M. Pastel, Application of information theory to the assessment of computed tomography., Med. Phys. 6, 83-94 (1979). 10.1118/1.594559
53. L. Loo, K. Doi, and C. Metz, A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads., Phys. Med. Biol. 29, 837-856 (1984). 10.1088/0031-9155/29/7/007
54. K. Myers, H. Barrett, M. Borgstrom, D. Patton, and G. Seeley, Effect of noise correlation on detectability of disk signals in medical imaging., J. Opt. Soc. Am. A 2, 1752-1759 (1985). 10.1364/JOSAA.2.001752
55. J. Rolland and H. Barrett, Effect of random background inhomogeneity on observer detection performance., J. Opt. Soc. Am. A 9, 649-658 (1992). 10.1364/JOSAA.9.000649
56. H. Barrett, J. Yao, J. Rolland, and K. Myers, Model observers for assessment of image quality., Proc. Natl. Acad. Sci. U.S.A. 90, 9758-9765 (1993). 10.1073/pnas.90.21.9758
57. A. Burgess, X. Li, and C. Abbey, Visual signal detectability with two noise components: anomalous masking effects., Journal of the Optical Society of America A 14 (9), 2420-2442 (1997). 10.1364/JOSAA.14.002420
58. A. Burgess, Visual signal detection with two-component noise: Low-pass spectrum effects., Journal of the Optical Society of America A 16 (3), 694-704 (1999). 10.1364/JOSAA.16.000694
59. F. Bochud, C. Abbey, and M. Eckstein, Visual signal detection in structured backgrounds. iii. calculation of figures of merit for model observers in statistically nonstationary backgrounds., Journal of the Optical Society of America A 17 (2), 193-205 (2000). 10.1364/JOSAA.17.000193
60. S. Riederer, N. Pelc, and D. Chesler, The noise power spectrum in computed x-ray tomography., Phys. Med. Biol. 23, 446-454 (1978). 10.1088/0031-9155/23/3/008
61. J. Baek and N. Pelc, The noise power spectrum in CT with direct fan beam reconstruction., Med. Phys. 37, 2074-2081 (2010). 10.1118/1.3378673
62. K. Hanson, Detectability in computed tomographic images., Med. Phys. 6, 441-451 (1979). 10.1118/1.594534
63. Z. Wang and A. Bovik, A universal image quality index., IEEE Signal Process. Lett. 9, 81-84 (2002). 10.1109/97.995823
64. J. Fessler and W. Rogers, Spatial resolution properties of penalized-likelihood image reconstruction: Space-invariant tomographs., IEEE Trans. Image Process. 5, 1346-1358 (1996). 10.1109/83.535846
65. P. Lauzier, J. Tang, M. Speidel, and G.-H. Chen, Noise spatial nonuniformity and the impact of statistical image reconstruction in CT myocardial perfusion imaging., Med. Phys. 39, 4079-4092 (2012). 10.1118/1.4722983
66. J. Siewerdsen, I. Cunningham, and D. Jaffray, A framework for noise-power spectrum analysis of multidimensional images., Med. Phys. 29, 2655-2671 (2002). 10.1118/1.1513158