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1.J. M. Boone and J. A. Seibert, “Monte Carlo simulation of the scattered radiation distribution in diagnostic radiology,” Med. Phys. 15, 713720 (1988).
2.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, 220231 (2001).
3.G. Jarry, S. A. Graham, D. J. Moseley, D. A. Jaffray, J. H. Siewerdsen, and F. Verhaegen, “Characterization of scattered radiation in kV CBCT images using Monte Carlo simulations,” Med. Phys. 33, 43204329 (2006).
4.G. J. Bootsma, F. Verhaegen, and D. A. Jaffray, “Spatial and angular frequency spectrum of the scatter distribution in CBCT,” Med. Phys. 40, 111901 (15pp.) (2013).
5.I. Kawrakow, “On the de-noising of Monte Carlo dose distributions,” Phys. Med. Biol. 47, 30873103 (2002).
6.J. O. Deasy, “Denoising of electron beam Monte Carlo dose distributions using digital filtering techniques,” Phys. Med. Biol. 45, 17651779 (2000).
7.J. O. Deasy, M. V. Wickerhauser, and M. Picard, “Accelerating Monte Carlo simulations of radiation therapy dose distributions using wavelet threshold de-noising,” Med. Phys. 29, 23662373 (2002).
8.I. El Naqa, I. Kawrakow, M. Fippel, J. V. Siebers, P. E. Lindsay, M. V. Wickerhauser, M. Vicic, K. Zakarian, N. Kauffmann, and J. O. Deasy, “A comparison of Monte Carlo dose calculation denoising techniques,” Physics in Medicine and Biology 50, 909922 (2005).
9.A. P. Colijn and F. J. Beekman, “Accelerated simulation of cone beam x-ray scatter projections,” IEEE Trans. Med. Imaging 23, 584590 (2004).
10.W. Zbijewski and F. J. Beekman, “Efficient Monte Carlo based scatter artifact reduction in cone-beam micro-CT,” IEEE Trans. Med. Imaging 25, 817827 (2006).
11.Y. Kyriakou and W. A. Kalender, “X-ray scatter data for flat-panel detector CT,” Med. Phys. 23, 315 (2007).
12.Y. Kyriakou, M. Meyer, and W. A. Kalender, “Technical note: Comparing coherent and incoherent scatter effects for cone-beam CT,” Phys. Med. Biol. 53, N175N185 (2008).
13.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, 897914 (2011).
14.G. J. Bootsma, F. Vehaegen, and D. A. Jaffray, “The effects of compensator design on scatter distribution and magnitude: A Monte Carlo study,” Proc. SPIE 7961, 796109-1796109-13 (2011).
15.S. A. Graham, D. J. Moseley, J. H. Siewerdsen, and D. A. Jaffray, “Compensator for dose and scatter management in cone-beam computed tomography,” Med. Phys. 34, 26912703 (2007).
16.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, 2232 (2009).
17.N. Freud, J. M. Letang, and D. Babot, “A hybrid approach to simulate multiple photon scattering in x-ray imaging,” Nucl. Instrum. Methods Phys. Res. B 227, 551558 (2005).
18.G. Pludniowski, P. M. Evans, V. N. Hansen, and S. Webb, “An efficient Monte Carlo-based algorithm for scatter correction in keV cone-beam CT,” Phys. Med. Biol. 54, 38473864 (2009).
19.E. Mainegra-Hing and I. Kawrakow, “Variance reduction techniques for fast Monte Carlo CBCT scatter correction calculations,” Phys. Med. Biol. 55, 44954507 (2010).
20.A. Badal and A. Badano, “Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit,” Med. Phys. 36, 48784880 (2009).
21.I. Kawrakow and D. W. O. Rogers, “The EGSnrc code system: Monte Carlo simulation of electron and photon transport,” National Research Council of Canada Technical Report No. PIRS-701 (National Research Council of Canada, Ottawa, 2000).
22.M. Frigo and S. G. Johnson, “The designa and implementation of FFTW3,” Proc. IEEE 93, 216231 (2005).
23.L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. A 1, 612619 (1984).
24.C. B. Chiarot, J. H. Siewerdsen, T. Haycocks, D. J. Moseley, and D. A. Jaffray, “An innovative phantom for quantitative and qualitative investigation of advanced x-ray imaging technologies,” Phys. Med. Biol. 50, N287N297 (2005).
25.D. W. O. Rogers, B. Faddegon, G. X. Ding, C. M. Ma, J. Wei, and T. R. Mackie, “Beam: A Monte Carlo code to simulate radiotherapy treatment units,” Med. Phys. 22, 503524 (1995).
26.H. Gao, R. Fahrig, N. R. Bennett, M. Sun, J. Star-Lack, and L. Zhu, “Scatter correction method for x-ray CT using primary modulation: Phantom studies,” Med. Phys. 37, 934946 (2010).
27.L. Zhu, N. R. Bennett, and R. Fahrig, “Scatter correction method for x-ray CT using primary modulation: Theory and preliminary results,” IEEE Trans. Med. Imaging 25, 15731587 (2006).
28.L. Zhu, J. Wang, and L. Xing, “Noise suppression in scatter correction for cone-beam CT,” Med. Phys. 36, 741753 (2009).
29.J. H. Siewerdsen and D. A. Jaffray, “A ghost story: Spatio-temporal response characteristics of an indirect-detection flat-panel imager,” Med. Phys. 33, 16241641 (1999).
30.N. Mail, D. J. Moseley, J. H. Siewerdsen, and D. A. Jaffray, “An empirical method for lag correction in cone-beam CT,” Med. Phys. 35, 51875196 (2008).
31.D. Yan, F. Vicini, J. Wong, and A. Martinez, “Adaptive radiation therapy,” Phys. Med. Biol. 42, 123132 (1997).
32.B. Haas, T. Coradi, M. Scholz, P. Kunz, M. Huber, U. Oppitz, L. André, V. Lengkeek, D. Huyskens, A. van esch, and R. Reddick, “Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies,” Phys. Med. Biol. 53, 17511771 (2008).
33.S. Chen, D. M. Lovelock, and R. J. Radke, “Segmenting the prostate and rectum in CT imagery using anatomical constraints,” Med. Image Anal. 15, 111 (2011).
34.M. Bazalova, L. Beaulieu, S. Palefsky, and F. Verhaegen, “Correction of CT artifacts and its influence on Monte Carlo dose calculations,” Med. Phys. 34, 21202132 (2007).
35.E. Mainegra-Hing and I. Kawrakow, “Fast Monte Carlo calculation of scatter corrections for CBCT images,” J. Phys.: Conf. Ser. 102, 16 (2008).
36.M. Frigo and S. G. Johnson, “The design and implementation of FFTW3,” Proc. IEEE 93, 216231 (2005).
37.A. Sisniega, M. Abella, E. Lage, M. Desco, and J. J. Vaquero, “Automatic Monte-Carlo based scatter correction for x-ray cone-beam CT using general purpose graphic processing units (GP-GPU): A feasibility study,” IEEE Nuclear Science Symposium and Medical Imaging Conference (Valencia, 2011), pp. 37053709.
38.W. S. Cleveland, “Robust locally weighted regression and smoothing scatterplots,” J. Am. Stat. Assoc. 74, 829836 (1979).
39.W. S. Cleveland and S. J. Devlin, “Locally weighted regression: An approach to regression analysis by local fitting,” J. Am. Stat. Assoc. 83, 596610 (1988).
40.W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes: The Art of Scientific Computing, 3rd ed. (Cambridge University Press, Cambridge, England, 2007).
41.R. S. Thing and E. Mainegra-Hing, “Optimizing cone beam CT scatter estimation in egs_cbct for a clinical and virtual chest phantom,” Med. Phys. 41(7), 071902 (7pp) (2014).

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X-ray scatter is a significant impediment to image quality improvements in cone-beam CT (CBCT). The authors present and demonstrate a novel scatter correction algorithm using a scatter estimation method that simultaneously combines multiple Monte Carlo (MC) CBCT simulations through the use of a concurrently evaluated fitting function, referred to as concurrent MC fitting (CMCF).

The CMCF method uses concurrently run MC CBCT scatter projection simulations that are a subset of the projection angles used in the projection set, , to be corrected. The scattered photons reaching the detector in each MC simulation are simultaneously aggregated by an algorithm which computes the scatter detector response, . is fit to a function, , and if the fit of is within a specified goodness of fit (GOF), the simulations are terminated. The fit, , is then used to interpolate the scatter distribution over all pixel locations for every projection angle in the set . The CMCF algorithm was tested using a frequency limited sum of sines and cosines as the fitting function on both simulated and measured data. The simulated data consisted of an anthropomorphic head and a pelvis phantom created from CT data, simulated with and without the use of a compensator. The measured data were a pelvis scan of a phantom and patient taken on an Elekta Synergy platform. The simulated data were used to evaluate various GOF metrics as well as determine a suitable fitness value. The simulated data were also used to quantitatively evaluate the image quality improvements provided by the CMCF method. A qualitative analysis was performed on the measured data by comparing the CMCF scatter corrected reconstruction to the original uncorrected and corrected by a constant scatter correction reconstruction, as well as a reconstruction created using a set of projections taken with a small cone angle.

Pearson’s correlation, , proved to be a suitable GOF metric with strong correlation with the actual error of the scatter fit, . Fitting the scatter distribution to a limited sum of sine and cosine functions using a low-pass filtered fast Fourier transform provided a computationally efficient and accurate fit. The CMCF algorithm reduces the number of photon histories required by over four orders of magnitude. The simulated experiments showed that using a compensator reduced the computational time by a factor between 1.5 and 1.75. The scatter estimates for the simulated and measured data were computed between 35–93 s and 114–122 s, respectively, using 16 Intel Xeon cores (3.0 GHz). The CMCF scatter correction improved the contrast-to-noise ratio by 10%–50% and reduced the reconstruction error to under 3% for the simulated phantoms.

The novel CMCF algorithm significantly reduces the computation time required to estimate the scatter distribution by reducing the statistical noise in the MC scatter estimate and limiting the number of projection angles that must be simulated. Using the scatter estimate provided by the CMCF algorithm to correct both simulated and real projection data showed improved reconstruction image quality.


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