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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. L. del Risco Norrlid, C. Rönnqvist, K. Fransson, R. Brenner, L. Gustafsson, F. Edling, and S. Kullander, “Calculation of the modulation transfer function for the X-ray imaging detector DIXI using Monte Carlo simulation data,” Nucl. Instrum. Methods Phys. Res. A 466(1), 209217 (2001).
3. P. F. Liaparinos, I. S. Kandarakis, D. A. Cavouras, H. B. Delis, and G. S. Panayiotakis, “Modeling granular phosphor screens by Monte Carlo methods,” Med. Phys. 33, 45024514 (2006).
4. T. T. Monajemi, B. G. Fallone, and S. Rathee, “Thick, segmented CdWO4-photodiode detector for cone beam megavoltage CT: A Monte Carlo study of system design parameters,” Med. Phys. 33, 45674577 (2006).
5. B.-J. Kim, G. Cho, B. K. Cha, and B. Kang, “An x-ray imaging detector based on pixel structured scintillator,” Radiat. Meas. 42(8), 14151418 (2007).
6. A. Teymurazyan and G. Pang, “Monte Carlo simulation of a novel water-equivalent electronic portal imaging device using plastic scintillating fibers,” Med. Phys. 39(3), 15181529 (2012).
7. D. Sharma and A. Badano, “Comparison of experimental, mantis, and hybrid mantis x-ray response for a breast imaging CsI detector,” Breast Imaging 7361, 5663 (2012).
8. H. Fujita et al., “A simple method for determining the modulation transfer function in digital radiography,” IEEE Trans. Med. Imaging 11(1), 3439 (1992).
9. S. Agostinelli et al., “GEANT4: A simulation toolkit,” Nucl. Instrum. Methods A506, 250303 (2003).
10. S. J. Blake, P. Vial, L. Holloway, P. B. Greer, A. L. McNamara, and Z. Kuncic, “Characterization of optical transport effects on EPID dosimetry using Geant4,” Med. Phys. 40, 041708 (14pp.) (2013).
11. M. Constantin, J. Perl, T. LoSasso, A. Salop, D. Whittum, A. Narula, M. Svatos, and P. J. Keall, “Modeling the truebeam linac using a CAD to Geant4 geometry implementation: Dose and IAEA-compliant phase space calculations,” Med. Phys. 38, 40184024 (2011).
12.International Electrotechnical Commission. Medical Electrical Equipment: Characteristics of Digital X-Ray Imaging Devices. Part 1: Determination of the Detective Quantum Efficiency. IEC 62220-1. Ref Type: Report. Geneva, Switzerland: International Electrotechnical Commission; 2003.
13. J. T. Dobbins III, E. Samei, N. T. Ranger, and Y. Chen, “Intercomparison of methods for image quality characterization. II. Noise power spectrum,” Med. Phys. 33, 14661475 (2006).
14. J. T. Dobbins III, “Image quality metrics for digital systems,” Handbook of Medical Imaging (SPIE Press, Bellingham, WA, 2000), Vol. 1, pp. 161222.
15. R. K. Swank, “Absorption and noise in x-ray phosphors,” J. Appl. Phys. 44(9), 41994203 (1973).
16. G. Lubberts, “Random noise produced by x-ray fluorescent screens,” J. Opt. Soc. Am. 58(11), 14751482 (1968).
17. C. W. E. Van Eijk et al., “Inorganic scintillators in medical imaging,” Phys. Med. Biol. 47(8), R85R106 (2002).
18. E. Abel, M. Sun, D. Constanin, R. Fahrig, and J. Star-Lack, “User-friendly, ultra-fast simulation of detector DQE(f),” Proc. SPIE 8668, 86683O (2013).
19. Y. Wang, L. E. Antonuk, Q. Zhao, Y. El-Mohri, and L. Perna, “High-DQE EPIDs based on thick, segmented BGO and CsI: Tl scintillators: Performance evaluation at extremely low dose,” Med. Phys. 36, 57075718 (2009).
20. J. H. Siewerdsen, L. E. Antonuk, Y. El-Mohri, J. Yorkston, W. Huang, and I. A. Cunningham, “Signal, noise power spectrum, and detective quantum efficiency of indirect-detection flat-panel imagers for diagnostic radiology,” Med. Phys. 25, 614628 (1998).
21. A. Sawant et al., “Segmented crystalline scintillators: An initial investigation of high quantum efficiency detectors for megavoltage x-ray imaging,” Med. Phys. 32, 30673083 (2005).
22. L. Liu, L. E. Antonuk, Q. Zhao, Y. El-Mohri, and H. Jiang, “Countering beam divergence effects with focused segmented scintillators for high DQE megavoltage active matrix imagers,” Phys. Med. Biol. 57(16), 53435358 (2012).
23. P. Munro and D. C. Bouius, “X-ray quantum limited portal imaging using amorphous silicon flat-panel arrays,” Med. Phys. 25, 689702 (1998).
24. D. W. O. Rogers, “Fluence to dose equivalent conversion factors calculated with EGS3 for electrons from 100 keV to 20 GeV and photons from 11 keV to 20 GeV,” Health Phys. 46, 891914 (1984).
25. C. Kirkby and R. Sloboda, “Comprehensive Monte Carlo calculation of the point spread function for a commercial a-Si EPID,” Med. Phys. 32, 11151127 (2005).
26. C. Kausch, B. Schreiber, F. Kreuder, R. Schmidt, and O. Dössel, “Monte Carlo simulations of the imaging performance of metal plate/phosphor screens used in radiotherapy,” Med. Phys. 26, 21132124 (1999).
27. G. G. Poludniowski and P. M. Evans, “Optical photon transport in powdered-phosphor scintillators. Part II. Calculation of single-scattering transport parameters,” Med. Phys. 40, 041905 (9pp.) (2013).
28. R. L. Weisfield, “Amorphous silicon TFT x-ray image sensors,” in Technical Digest of IEEE International Electron Devices Meeting, IEDM'98, 1998 (IEEE, Piscataway, NJ, 1998), pp. 2124.
29. J. V. Siebers, J. Oh Kim, L. Ko, P. J. Keall, and R. Mohan, “Monte Carlo computation of dosimetric amorphous silicon electronic portal images,” Med. Phys. 31, 21352146 (2004).
30. Y. Wang, Y. El-Mohri, L. E. Antonuk, and Q. Zhao, “Monte Carlo investigations of the effect of beam divergence on thick, segmented crystalline scintillators for radiotherapy imaging,” Phys. Med. Biol. 55, 36593673 (2010).
31. A. Badano, R. M. Gagne, B. D. Gallas, R. J. Jennings, J. S. Boswell, and K. J. Myers, “Lubberts effect in columnar phosphors,” Med. Phys. 31, 31223131 (2004).
32. I. A. Cunningham, “Applied linear-systems theory,” Handbook of Medical Imaging (SPIE Press, Bellingham, WA, 2000), Vol. 1, pp. 79159.
33. S. Haykin, Modern Filters (MacMillan, New York, NY, 1989).
34. M. K. Cho, H. K. Kim, T. Graeve, S. M. Yun, C. H. Lim, H. Cho, and J.-M. Kim, “Measurements of x-ray imaging performance of granular phosphors with direct-coupled CMOS sensors,” IEEE Trans. Nucl. Sci. 55(3), 13381343 (2008).

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Performance optimization of indirect x-ray detectors requires proper characterization of both ionizing (gamma) and optical photon transport in a heterogeneous medium. As the tool of choice for modeling detector physics, Monte Carlo methods have failed to gain traction as a design utility, due mostly to excessive simulation times and a lack of convenient simulation packages. The most important figure-of-merit in assessing detector performance is the detective quantum efficiency (DQE), for which most of the computational burden has traditionally been associated with the determination of the noise power spectrum (NPS) from an ensemble of flood images, each conventionally having 107 − 109 detected gamma photons. In this work, the authors show that the idealized conditions inherent in a numerical simulation allow for a dramatic reduction in the number of gamma and optical photons required to accurately predict the NPS.

The authors derived an expression for the mean squared error (MSE) of a simulated NPS when computed using the International Electrotechnical Commission-recommended technique based on taking the 2D Fourier transform of flood images. It is shown that the MSE is inversely proportional to the number of flood images, and is independent of the input fluence provided that the input fluence is above a minimal value that avoids biasing the estimate. The authors then propose to further lower the input fluence so that each event creates a point-spread function rather than a flood field. The authors use this finding as the foundation for a novel algorithm in which the characteristic MTF(f), NPS(f), and DQE(f) curves are simultaneously generated from the results of a single run. The authors also investigate lowering the number of optical photons used in a scintillator simulation to further increase efficiency. Simulation results are compared with measurements performed on a Varian AS1000 portal imager, and with a previously published simulation performed using clinical fluence levels.

On the order of only 10–100 gamma photons per flood image were required to be detected to avoid biasing the NPS estimate. This allowed for a factor of 107 reduction in fluence compared to clinical levels with no loss of accuracy. An optimal signal-to-noise ratio (SNR) was achieved by increasing the number of flood images from a typical value of 100 up to 500, thereby illustrating the importance of flood image quantity over the number of gammas per flood. For the point-spread ensemble technique, an additional 2× reduction in the number of incident gammas was realized. As a result, when modeling gamma transport in a thick pixelated array, the simulation time was reduced from 2.5 × 106 CPU min if using clinical fluence levels to 3.1 CPU min if using optimized fluence levels while also producing a higher SNR. The AS1000 DQE(f) simulation entailing both optical and radiative transport matched experimental results to within 11%, and required 14.5 min to complete on a single CPU.

The authors demonstrate the feasibility of accurately modeling x-ray detector DQE(f) with completion times on the order of several minutes using a single CPU. Convenience of simulation can be achieved using GEANT4 which offers both gamma and optical photon transport capabilities.


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