Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. E. P. Rührnschopf and K. Klingenbeck, “A general framework and review of scatter correction methods in cone-beam CT. Part 2: Scatter estimation approaches,” Med. Phys. (accepted for publication 2011).
2. P. Stonestrom and A. Macovski, “Scatter considerations in fan beam computerized tomographic systems,” IEEE Trans. Nucl. Sci. NS-23(5), 14531458 (1976).
3. P. C. Johns and M. Yaffe, “Scattered radiation in fan beam imaging systems,” Med. Phys. 9(2), 231239 (1982).
4. P. M. Joseph and R. D. Spital, “The effects of scatter in x-ray computed tomography,” Med. Phys. 9(4), 464472 (1982).
5. G. H. Glover, “Compton scatter effects in CT reconstructions,” Med. Phys. 9(6), 860867 (1982).
6. R. P. Moran, W. S. Y. Kwa, and S. A. G. Chenery, “The influence of scattered radiation on the CT numbers of bone on a scanner with a fixed detector array,” Phys. Med. Biol. 28, 939951 (1983).
7. B. Ohnesorge, T. Flohr, and K. Klingenbeck-Regn, “Efficient object scatter correction algorithm for third and fourth generation CT scanners,” Eur. Radiol. 9(3), 563569 (1999).
8. J. H. Siewerdsen and D. A. Jaffray, “Cone-beam computed tomography with a flat-panel imager: Magnitude and effects of x-ray scatter,” Med. Phys. 28(2), 220231 (2001).
9. H. Aichinger,J. Dierker, S. Joite-Barfuss, and M. Saebel. Radiation Exposure and Image Quality in X-ray Diagnostic Radiology (Springer, Berlin, New York, 2004).
10. E. P. Rührnschopf, G. Schwierz, and H. Eschenbacher, “Nonlinearity and inhomogenity effects in computerized tomography due to the exponential attenuation of radiation,” in Mathematical Aspects of Computerized Tomography, edited by G. T. Herman and F. Natterer (Springer, Berlin, New York, 1981), pp. 252269.
11. Mathematical Aspects of Computerized Tomography. Proceedings Oberwolfach 1980, edited by G. T. Herman and F. Natterer, Vol. 8 of Lecture Notes in Medical Informatics (Springer, Berlin, New York, 1981).
12. C. E. Floyd, P. T. Beatty, and C. E. Ravin, “Scatter compensation in digital chest radiography using Fourier deconvolution,” Invest. Radiol. 24, 3033 (1989).
13. J.-Y. Jin, L. Ren, Q. Liu, J. Kim, N. Wen, H. Guan, B. Movsas, and I. J. Chetty, “Combining scatter reduction and correction to improve image quality in cone-beam computed tomography (CBCT),” Med. Phys. 37(11), 56345644 (2010).
14. K. P. Maher and J. F. Malone, “Computerized scatter correction in diagnostic radiology,” Contemp. Phys. 38(2), 131148 (1997).
15. S. A. Graham, D. J. Moseley, J. H. Siewerdsen, and D. A. Jaffray, “Compensators for dose and scatter management in cone-beam computed tomography,” Med. Phys. 34, 26912703 (2007).
16. D. Lazos, G. Lasio, J. Evans, and J. F. Williamson, “Evaluation of scatter mitigation strategies for X-ray cone-beam-CT: Impact of scatter subtraction and anti-scatter grids on contrast-to-noise ratio,” Proc. SPIE 6510, 65102V (2007).
17. G. T. Barnes, H. M. Cleare, and I. A. Brezovich, “Reduction of scatter in diagnostic radiology by means of a scanning multiple slit assembly,” Radiology 120, 691694 (1976).
18. K. M. Ogden,C. R. Wilson, and R. Cox, “Contrast improvements in digital radiography using a scatter reduction processing algorithm,” Proc. SPIE 4684, 10341047 (2002).
19. J. A. Sorenson and J. Floch, “Scatter rejection by air gaps: An empirical model,” Med. Phys. 12(3), 308316 (1985).
20. J. A. Seibert and J. M. Boone, “X-ray scatter removal by deconvolution,” Med. Phys. 15(3), 567575 (1988).
21. Z. Liu, T. Obi, M. Yamaguchi, and N. Ohyama, “A fast estimation of scatter components with good accuracy by using OS-EM techniques for scatter subtraction,” Proc. SPIE 3659, 962973 (1999).
22. J. A. Seibert and J. M. Boone, “Medical image scatter suppression by inverse filtering,” Proc. SPIE 914, 742750 (1988).
23. R. Ning, X. Tang, and D. L. Conover, “X-ray scatter suppression algorithm for cone beam volume CT,” Proc. SPIE 4682, 774781 (2002).
24. W. A. Kalender, “Determination of the intensity of scattered radiation and the performance of grids in diagnostic radiology,” PhD thesis, University of Wisconsin-Madison, 1979, Wisconsin Medical Physics Report No. WMP-102.
25. G. T. Barnes, “Contrast and scatter in x-ray imaging,” Radiographics 11, 307323 (1991).
26. L. Chen, C. C. Shaw, M. C. Altunbas, C.-J. Lai, X. Liu, T. Han, T. Wang, W. T. Yang, and G. J. Whitman, “Feasibility of volume-of-interest (VOI) scanning technique in cone beam breast CT—A preliminary study,” Med. Phys. 35, 34823490 (2008).
27. C.-J. Lai, L. Chen, H. Zhang, X. Liu, Y. Zhong, Y. Shen, T. Han, S. Ge, Y. Yi, T. Wang, W. T. Yang, G. J. Whitman, and C. C. Shaw, “Reduction in x-ray scatter and radiation dose for volume-of-interest (VOI) cone-beam breast CT—A phantom study,” Phys. Med. Biol. 54, 66916709 (2009).
28. R. N. Chityala, K. R. Hoffmann, D. R. Bednarek, and Rudin S. Region-of-interest ROI computed tomography,” Proc. SPIE 5368, 534541 (2004).
29. R. N. Chityala, K. R. Hoffmann, Rudin S , and D. R. Bednarek, “Region-of-interest ROI computed tomography CT: Comparison with full field of view and truncated CT for a human head phantom,” Proc. SPIE 5745, 583590 (2005).
30. O. Pasche, “Über eine neue Blendenvorrichtung der Röntgentechnik,” Dtsch. Med. Wochenschr. 29, 266267 (1903).
31. P. M. Shikhaliev, “Computed tomography with energy-resolved detection: A feasibility study,” Phys. Med. Biol. 53, 14751495 (2008).
32. E. G. Solomon, M. S. Van Lysel, R. E. Melen, J. W. Moorman, and Skillicorn B , “Low exposure scanning-beam x-ray fluoroscopy system,” Proc. SPIE 2708, 140149 (1996).
33. M. S. Van Lysel, E. G. Solomon, B. P. Wilfley, Dutta A , and M. A. Speidel, “Performance assessment of the scanning-beam digital x-ray (SBDX) system,” Proc. SPIE 3032, 161170 (1997).
34. M. A. Speidel, B. P. Wilfley, J. M. Star-Lack, J. A. Heanue, and M. S. Van Lysel, “Scanning-beam digital x-ray (SBDX) technology for interventional and diagnostic cardiac angiography,” Med. Phys. 33(8), 27142727 (2006).
35. M. A. Speidel, B. P. Wilfley, J. M. Star-Lack, J. A. Heanue, T. D. Betts, and M. S. Van Lysel, “Comparison of entrance exposure and signal-to-noise ratio between an SBDX prototype and a widebeam cardiac angiographic system,” Med. Phys. 33(8), 27282743 (2006).
36. R. Bhagtani and T. G. Schmidt, “Simulated scatter performance of an inverse-geometry dedicated breast CT,” Med. Phys. 36(3), 788796 (2009).
37. T. G. Schmidt, R. Fahrig, N. J. Pelc, and E. G. Solomon, “An inverse-geometry volumetric CT system with a large-area scanned source: A feasibility study,” Med. Phys. 31, 26232627 (2004).
38. J. E. Tkaczyk, Y. Du, D. Walter, X. Wu, J. Li, and T. Toth, “Simulation of CT dose and contrast-to-noise as a function of bow-tie shape,” Proc. SPIE 5368, 403410 (2004).
39. A. L. C. Kwan, J. M. Boone, and N. Shah. “Evaluation of x-ray scatter properties in a dedicated cone-beam breast CT scanner,” Med. Phys. 32(9), 29672975 (2005).
40. N. Mail, D. J. Moseley, J. H. Siewerdsen, and D. A. Jaffray, “The influence of bowtie filtration on cone-beam CT image quality,” Med. Phys. 36(1), 2232 (2009).
41. B. Menser, J. Wiegert, S. Wiesner, and M. Bertram. “Use of beam shapers for cone-beam CT with off-centered flat detector,” in Proceedings of SPIE 2010, Proc. SPIE 7622, 762233 (2010).
42. G. J. Bootsma, F. Verhaegen, and D. A. Jaffray, “The effects of compensator and imaging geometry on the distribution of x-ray scatter in CBCT,” Med. Phys. 38(2), 897914 (2011).
43. J. M. Boone, “Method for evaluating bow tie filter angle-dependent attenuation in CT: Theory and simulation results,” Med. Phys. 37(1), 4048 (2010).
44. F. M. Groedel and R. Wachter, “Bedeutung der Röhren-Fern- und Platten-Abstandsaufnahmen,” Verh d Dt Röntgengesellschaft 17 134135 (1926).
45. F. Janus, “Die Bedeutung der gestreuten ungerichteten Röntgenstrahlen fur die Bildwirkung und ihre Beseitigung,” Verh d Dt Röntgengesellschaft 17, 136139 (1926).
46. U. Neitzel, “Grids or air gaps for scatter reduction in digital radiography: A model calculation,” Med. Phys. 19(2), 475481 (1992).
47. J. Persliden and G. A. Carlsson, “Scatter rejection by air gaps in diagnostic radiology. Calculations using Monte Carlo collision density method and consideration of molecular interference in coherent scattering,” Phys. Med. Biol. 42, 155175 (1997).
48. G. Bucky, “Über die Ausschaltung der im Objekt entstehenden Sekundärstrahlen bei Röntgenstrahlen,” Verh d Dt Röntgengesellschaft 9, 3032 (1913).
49. W. A. Kalender, “Calculation of x-ray grid characteristics by Monte Carlo methods,” Phys. Med. Biol. 27(3), 353361 (1982).
50. H. P. Chan and K. Doi, “Investigation of the performance of antiscatter grids: Monte Carlo simulation studies,” Phys. Med. Biol. 27, 785 (1982).
51. H. P. Chan, Y. Higashida, and K. Doi, “Performance of antiscatter grids radiation in diagnostic radiology: Experimental measurements and Monte Carlo simulation studies,” Med. Phys. 12(4), 449454 (1985).
52. H. P. Chang, K. L. Lam, and Y. Wu, “Studies of performance of antiscatter grids in digital radiography: Effect on signal-to-noise ratio,” Med. Phys. 17(4), 655664 (1990).
53. G. Vogtmeier, R. Dorscheid, K. J. Engel, R. Luhta, R. Mattson, B. Harwood, M. Appleby, B. Randolph, and J. Klinger, “Two-dimensional anti-scatter grids for computed tomography detectors,” Proc. SPIE 6913, 691359 (2008).
54. J. H. Siewerdsen, D. J. Moseley, B. Bakhtiar, S. Richard, and D. A. Jaffray, “The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors,” Med. Phys. 31(12), 35063520 (2004).
55. J. Wiegert, M. Bertram, D. Schaefer, N. Conrads, J. Timmer, T. Aach, and G. Rose, “Performance of standard fluoroscopy antiscatter grids in flat-panel based cone-beam CT,” Proc. SPIE 5368, 6778 (2004).
56. Y. Kyriakou and W. Kalender, “Efficiency of antiscatter grids for flat-detector CT,” Phys. Med. Biol. 52, 62756293 (2007).
57. J. Rinkel, L. Gerfault, F. Esteve, and J. M. Dinten, “Coupling the use of anti-scatter grid with analytical scatter estimation in cone beam CT,” in Proc. SPIE 6510, 65102E (2007).
58. J. Wiegert and M. Bertram. “Scattered radiation in flat-detector based cone-beam CT: Analysis of voxelized patient simulations,” Proc. SPIE 6142, 614235 (2006).
59. M. Meyer, W. A. Kalender, and Y. Kyriakou, “A fast and pragmatic approach for scatter correction in flat-detector CT using elliptic modeling and iterative optimization,” Phys. Med. Biol. 55, 99120 (2010).
60. Y. Censor, T. Elfving, and G. T. Herman, “A method of iterative data refinement and its applications,” Math. Methods Appl. Sci. 7, 108123 (1985).
61. M. Defrise and G. T. Gullberg, “Review: Image reconstruction,” Phys. Med. Biol. 51, R139R154 (2006).
62. L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. A, 612619 (1984).
63. K. Wiesent, K. Barth, P. Durlak, T. Brunner, O. Schuetz, and W. Seissler, “Enhanced 3-D-reconstruction algorithm for C-arm systems suitable for interventional procedures,” IEEE Trans. Med. Imaging 19(5), 391403 (2000).
64. A. Katsevich, “Analysis of an exact inversion algorithm for spiral cone-beam CT,” Phys. Med. Biol. 47, 25832597 (2002).
65. R. Grimmer and M. Kachelriess, “Empirical binary tomography calibration (EBTC) for the pre-correction of beam hardening and scatter for flat panel CTMed. Phys. 38(4), 22332240 (2011).
66. E. Mainegra-Hing and I. Kawrakow. “Fast Monte Carlo calculation of scatter corrections for CBCT images,” J. Phys.: Conf. Ser., 102, 16 (2008), Third McGill International Workshop.
67. H. Bruder, R. Raupach, J. Sunnegardh, M. Sedlmair, H. Wallschlager, K. Stierstorfer, and T. Flohr, “A new class of regularization priors for iterative reconstruction in cone-beam CT,” in Proceedings of 10th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine and 2nd HPIR Workshop, Beijing, China, 5–10 September, 2009.
68. H. Bruder, R. Raupach, M. Sedlmair, F. Wursching, K. Schwarz, K. Stierstorfer, and T. Flohr, “Toward iterative reconstruction in clinical CT: Increased sharpness-to-noise and significant dose reduction owing to a new class of regularization priors,” Proc. SPIE 7622, 76222V (2010).
69. R. C. Puetter, T. R. Gosnell, and A. Yahil, “Digital image reconstruction: Deblurring and Denoising,” Annu. Rev. Astron. Astrophys. 43, 139194 (2005).
70. M. C. Altunbas, C. Shaw, L. Chen, C. Lai, X. Liu, T. Han, and T. Wang, “A post-reconstruction method to correct cupping artifacts in cone beam breast computed tomography,” Med. Phys. 34, 31093118 (2007).
71. T. E. Marchant, C. J. Moore, C. G. Rowbottom, R. I. MacKay, and P. C. Williams. “Shading correction algorithm for improvement of cone-beam CT images in radiotherapy,” Phys. Med. Biol. 53, 57195733 (2008).
72. Y. Kyriakou, M. Meyer, R. M. Lapp, and W. A. Kalender. “Histogram-driven cupping correction HDDC in CT,” In Proc. SPIE 7622, 76221S (2010).
73. J. Wiegert, S. Hohmann, and M. Bertram, “Iterative scatter correction based on artifact assessment,” Proc. SPIE 6913, 69132B (2008).
74. M. Zellerhoff, B. Scholz, E. P. Ruehrnschopf, and T. Brunner, “Low contrast 3D reconstruction from C-arm data,” Proc. SPIE 5745, 646655 (2005).
75. V. N. Hansen, W. Swindell, and P. M. Evans, “Extraction of primary signal from EPIDs using only forward convolution,” Med. Phys. 24(9), 14771484 (1997).
76. J. S. Maltz, B. Gangadharan, S. Bose, D. H. Hristov, B. A. Faddegon, A. Paidi, and A. R. Bani-Hashemi, “Algorithm for x-ray scatter, beam-hardening, and beam profile correction in diagnostic (kilovoltage) and treatment (megavoltage) cone beam CT,” IEEE Trans. Med. Imaging 27, 17911810 (2008).
77. I. Reitz, B.-M. Hesse, S. Nill, T. Tucking, and U. Oelfke, “Enhancement of image quality with a fast iterative scatter and beam hardening correction method for kV CBCT,” Zeitschrift fur Medizinische Physik 19, 158172 (2009).
78. H. H. Barrett and W. Swindell, Radiological Imaging. The Theory of Image Formation, Detection, and Processing (Academic Press, New York, London, 1981), 2 volumes.
79. R. A. Close, K. C. Shah, and J. S. Whiting, “Regularization method for scatter-glare correction in fluoroscopic images,” Med. Phys. 26(9), 17941801 (1999).
80. P. Abbott, A. Shearer, T. O’Doherty, and W. van der Putten, “Image deconvolution as an aid to mammographic artefact identification I: Basic techniques,” Proc. SPIE 3661, 698709 (1999).
81. W. Yao and K. W. Leszczynski, “An analytical approach to estimating the first order X-ray scatter in heterogeneous medium. II. A practical application,” Med. Phys. 36, 31573167 (2009).
82. Multislice CT, 3rd ed., edited by M. F. Reiser, C. R. Becker, K. Nikolaou, and G. Glazer (Springer, Berlin, New York, 2009).
83. M. Xia, G. D. Tourassi, J. Y. Lo, and C. E. Floyd, “On the development of a Gaussian noise model for scatter compensation,” Proc. SPIE 6510, 65102M (2007).
84. F. Natterer and F. Wübbeling, in Mathematical Methods in Image Reconstruction, SIAM Monographs on Mathematical Modeling and Computation (SIAM, Philadelphia, 2001).
85. L. A. Shepp and Y. Vardi, “Maximum likelihood reconstruction for emission tomography,” IEEE Trans. Med. Imaging 1, 113122 (1982).
86. C. E. Floyd, A. H. Baydush, J. Y. Lo, J. E. Bowsher, and C. E. Ravin, “Scatter compensation for digital chest radiography using maximum likelihood expectation maximization,” Invest. Radiol. 28, 427433 (1993).
87. A. H. Baydush and C. E. Floyd, “The effect of maximum likelihood—Median processing on the contrast-to-noise ratio in digital chest radiography,” Proc. SPIE 2167, 586592 (1994).
88. A. H. Baydush, J. E. Bowsher, J. K. Laading, and C. E. Floyd, “Improved Bayesian image estimation for digital chest radiography,” Med. Phys. 24(4), 539545 (1997).
89. C. E. Metz and C.-T. Chen, “On the acceleration of maximum likelihood algorithms,” Proc. SPIE 914, 344349 (1988).
90. W. B. Richardsen, “Bayesian-based iterative method of image restoration,” J. Opt. Soc. Am. 62, 5559 (1972).
91. L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79, 745754 (1974).
92. A. P. Colijn and F. J. Beekman, “Accelerated simulation of cone beam x-ray CT scatter projections,” IEEE Trans. Med. Imaging 23(5), 584590 (2004).
93. W. Zbijewski and F. J. Beekman, “Efficient Monte Carlo based scatter artefact reduction cone-beam micro-CT,” IEEE Trans. Med. Imaging 25(7), 817827 (2006).
94. H. Li, R. Mohan, and X. R. Zhu, “Scatter kernel estimation with an edge-spread function method for cone-beam computed tomography imaging,” Phys. Med. Biol. 53, 67296748 (2008).
95. B. W. Silverman, M. C. Jones, D. W. Nychka, and J. D. Wilson, “A smoothed EM approach to indirect estimation problems, with particular reference to stereology and emission tomography,” J. R. Stat. Soc. Ser. B 52, 271324 (1990).
96. P. J. Green, “On the use of the EM algorithm for penalized likelihood estimation,” J. R. Stat. Soc. Ser. B 52, 443452 (1990).
97. B. R. Frieden, “The controversy between Bayesians and classicists,” in Probability, Statistical Optics, and Data Testing (Springer, Berlin, New York, 1991), Chap. 16, pp. 363373.
98. C. E. Floyd, A. H. Baydush, J. Y. Lo, J. E. Bowsher, and C. E. Ravin. “Bayesian restoration of chest radiographs: Scatter compensation with improved signal to noise ratio,” Invest. Radiol. 29, 904910 (1994).
99. A. H. Baydush and C. E. Floyd. “Bayesian image estimation of digital chest radiography: Interdependence of noise resolution and scatter fraction,” Med. Phys. 22(8), 12551261 (1995).
100. A. H. Baydush and C. E. Floyd, “Spatially varying Bayesian image estimation,” Acad. Radiol. 3, 129136 (1996).
101. A. H. Baydush, W. C. Gehm, and C. E. Floyd, “Comparison of contrast-to-noise ratios for Bayesian processing grids in digital chest radiography,” Proc. SPIE 3661, 665672 (1999).
102. A. H. Baydush and C. E. Floyd, “Improved image quality with Bayesian image processing in digital mammography,” Proc. SPIE 3979, 781786 (2000).
103. A. H. Baydush and C. E. Floyd, “Improved image quality in digital mammography with image processing,” Med. Phys. 27(7), 15031508 (2000).
104. T. Hebert and R. Leahy, “A generalized EM algorithm for 3D Bayesian reconstruction from Poisson data using Gibbs priors,” IEEE. Trans. Med. Imaging 8, 194202 (1989).
105. L. Zhu, J. Wang, and L. Xing, “Noise suppression in scatter correction for cone-beam CT,” Med. Phys. 36(3), 741752 (2009).
106. J. Wang, T. Li, D. Eremina, D. Zhang, S. Wang, J. Chen, J. Manzione, and Z. Liang, “An experimental study on the noise properties of x-ray CT sinogram data in the Radon space,” Phys. Med. Biol. 53, 33273341 (2008).
107. J. Wang, T. Li, H. Lu, and Z. Liang, “Noise reduction for low-dose helical CT by fully 3D penalized weighted least-squares sinogram smoothing,” Proc. SPIE 6142, 61424E (2006).
108. J. Wang, T. Li, H. Lu, and Z. Liang, “Noise reduction for low-dose single-slice helical CT sinograms,” IEEE Trans. Nucl. Sci. 53, 1230 (2006).
109. E. P. Ruehrnschopf and W. Haerer, “Scatter radiation correction in radiography and computed tomography employing flat panel detector,” U.S. patent 7,860,208,B2 (28 December 2010), Technical Report, Foreign Application Priority Data, September 29 (2006).
110. M. Petersilka, K. Stierstorfer, H. Bruder, and T. Flohr, “Strategies for scatter correction in dual source CT,” Med. Phys. 37(11), 59715992 (2010).
111. M. Endo, T. Tsunoo, N. Nakamori, and K. Yoshida, “Effect of scattered radiation on image noise in cone beam CT,” Med. Phys. 28(4), 469474 (2001).
112. M. Endo, S. Mori, T. Tsunoo, and H. Miyazaki, “Magnitude and effects of x-ray scatter in a 256-slice CT scanner,” Med. Phys. 33(9), 33593368 (2006).
113. J. Wiegert. “Scattered radiation in cone-beam computed tomography: Analysis, quantification and compensation,” Ph.D. thesis, Aachen University of Technology (RWTH), Aachen, Germany, 2007.
114. M. Wiegert, S. Hohmann, and M. Bertram, “Scattered radiation in flat-detector based cone-beam CT: Propagation of signal, contrast and noise into reconstructed volumes,” Proc. SPIE 6510, 112 (2007).
115. S. T. Schmidt, “CT energy weighting in the presence of scatter and limited energy resolution,” Med. Phys. 37(3), 10561067 (2010).
116. W. Swindell and P. M. Evans, “Scattered radiation in portal images: A Monte Carlo simulation and a simple physical model,” Med. Phys. 23(1), 6373 (1996).
117. D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34(11), 44594471 (2007).
118. P. Bernhardt, L. Baetz, E. P. Ruehrnschopf, and M. Hoheisel, “Spatial frequency-dependent signal-to-noise ratio as a generalized measure of image quality,” Proc. SPIE 5745, 407418 (2005).
119. Handbook of Medical Imaging, edited by J. Beutel, H. L. Kundel, and R. L. VanMetter (SPIE Press, Bellingham, Washington, 2000).
120. K. J. Engel, C. Herrmann, and G. Zeitler, “X-ray scattering in single- and dual-source CT,” Med. Phys. 35, 318332 (2008).
121. J. H. Siewerdsen and D. A. Jaffray, “Optimization of x-ray imaging geometry (with specific application to flat-panel cone-beam computed tomography),” Med. Phys. 27(8), 19031914 (2000).
122. I. S. Kyprianou, S. Rudin, D. R. Bednarek, and K. R. Hoffmann, “Generalizing the MTF and DQE to include x-ray scatter and focal spot unsharpness: Application to a new microangiographic system,” Med. Phys. 32(2), 613626 (2005).
123. A. Jain, A. T. Kuhl-Gilcrist, S. K. Gupta, D. R. Bednarek, and S. Rudin, “Generalized two-dimensional (2D) linear system analysis metrics (GMTF,GDQE) for digital radiography systems including effect of focal spot, magnification, scatter, and detector characteristics,” Proc. SPIE 7622, 7622 0K–1 (2010).
124. N. T. Ranger, A. Mackenzie, I. D. Honey, J. T. Dobbins, C. E. Ravin, and Samei E , “Extension of DQE to include scatter, grid, magnification, and focal spot blur: A new experimental technique and metric,” Proc. SPIE 7258, 725831A (2009).
125. C. Lanczos, Applied Analysis, 2nd ed. (Prentice-Hall, Englewood Cliffs, NJ, 1961).

Data & Media loading...


Article metrics loading...



Since scattered radiation in cone-beam volume CT implies severe degradation of CTimages by quantification errors, artifacts, and noise increase, scatter suppression is one of the main issues related to image quality in CBCTimaging. The aim of this review is to structurize the variety of scatter suppression methods, to analyze the common structure, and to develop a general framework for scatter correction procedures. In general, scatter suppression combines hardware techniques of scatter rejection and software methods of scatter correction. The authors emphasize that scatter correction procedures consist of the main components scatter estimation (by measurement or mathematical modeling) and scatter compensation (deterministic or statistical methods). The framework comprises most scatter correction approaches and its validity also goes beyond transmission CT. Before the advent of cone-beam CT, a lot of papers on scatter correction approaches in x-ray radiography, mammography, emission tomography, and in Megavolt CT had been published. The opportunity to avail from research in those other fields of medical imaging has not yet been sufficiently exploited. Therefore additional references are included when ever it seems pertinent. Scatter estimation and scatter compensation are typically intertwined in iterative procedures. It makes sense to recognize iterative approaches in the light of the concept of self-consistency. The importance of incorporating scatter compensation approaches into a statistical framework for noise minimization has to be underscored. Signal and noise propagation analysis is presented. A main result is the preservation of differential-signal-to-noise-ratio (dSNR) in CT projection data by ideal scatter correction. The objective of scatter compensation methods is the restoration of quantitative accuracy and a balance between low-contrast restoration and noise reduction. In a synopsis section, the different deterministic and statistical methods are discussed with respect to their properties and applications. The current paper is focused on scatter compensation algorithms. The multitude of scatter estimation models will be dealt with in a separate paper.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd