General purpose Monte Carlo code for simulation of particle transport is used to study the basic dosimetric parameters like percentage depth dose and dose profiles and compared with the experimental measurements from commercial dual energy medical linear accelerator. Varian Clinac iX medical linear accelerator with dual energy photon beams (6 and 15？MV) is simulated using FLUKA. FLAIR is used to visualize and edit the geometry. Experimental measurements are taken for 100？cm source-to-surface (SSD) in 50 × 50 × 50？cm3 PTW water phantom using 0.12？cc cylindrical ionization chamber. Percentage depth dose for standard square field sizes and dose profiles for various depths are studied in detail. The analysis was carried out using ROOT (a DATA analysis frame work developed at CERN) system. Simulation result shows good agreement in percentage depth dose and beam profiles with the experimental measurements for Varian Clinac iX dual energy medical linear accelerator. 1. Introduction Monte Carlo (MC) method has become a powerful tool in radiation therapy for studying the dosimetric parameters. It is a common objective of medical physics to achieve an accuracy of better than ±5% for the delivery of dose. But this can be realized only if the dose calculation accuracy is better than ±2%. Therefore, in the future, Monte Carlo algorithms will have a clear preference compared with all other methods of dose calculation. At present, different Monte Carlo codes are used widely for modeling medical linear accelerators [1–4]. Monte Carlo methods are applied in radiation therapy to analyze the adequacy of linac head components, to benchmark dose calculation models, and to study the beam characteristics [5, 6]. Modeling of medical linear accelerator in clinical environments using any Monte Carlo models is quite complex to address source definition and collimator materials definitions. Commercial manufactures of medical linear accelerators are distributing limited versions with confidence of agreements. Different approaches are suggested in many literatures to quantify this limited knowledge for modeling [7, 8]. Percentage depth dose (PDD) and beam profiles are the basic parameters to ensure the adequacy of Monte Carlo modeling [9]. Monte Carlo for treatment planning is practically impossible in Indian scenarios, where there are a large number of patients with limited facilities. A Monte Carlo model takes considerable time to optimize individual treatment plans. However, the beam characteristic and other parameters can be tuned using any available Monte Carlo codes for all
References
[1]
D. Sheikh-Bagheri and D. W. O. Rogers, “Sensitivity of megavoltage photon beam Monte Carlo simulations to electron beam and other parameters,” Medical Physics, vol. 29, no. 3, pp. 379–390, 2002.
[2]
F. Verhaegen and J. Seuntjens, “Monte Carlo modelling of external radiotherapy photon beams,” Physics in Medicine and Biology, vol. 48, no. 21, pp. R107–R164, 2003.
[3]
A. Mesbahi, M. Fix, M. Allahverdi, E. Grein, and H. Garaati, “Monte Carlo calculation of Varian 2300C/D Linac photon beam characteristics: a comparison between MCNP4C, GEANT3 and measurements,” Applied Radiation and Isotopes, vol. 62, no. 3, pp. 469–477, 2005.
[4]
A. Mesbahi, A. J. Reilly, and D. I. Thwaites, “Development and commissioning of a Monte Carlo photon beam model for Varian CL 2100EX linear accelerator,” Applied Radiation and Isotopes, vol. 64, no. 6, pp. 656–662, 2006.
[5]
T. D. Solberg, J. J. DeMarco, I. J. Chetty et al., “A review of radiation dosimetry applications using the MCNP Monte Carlo code,” Radiochimica Acta, vol. 89, no. 4-5, pp. 337–355, 2001.
[6]
M. K. Fix, P. Manser, E. J. Born, R. Mini, and P. Rüegsegger, “Monte Carlo simulation of a dynamic MLC based on a multiple source model,” Physics in Medicine and Biology, vol. 46, no. 12, pp. 3241–3257, 2001.
[7]
S. Y. Lin, T. C. Chu, and J. P. Lin, “Simulation of a clinical linear accelerator,” Applied Radiation and Isotopes, vol. 55, pp. 759–765, 2001.
[8]
S. B. Jiang, T. Pawlicki, and C. M. Ma, “Removing the effect of statistical uncertainty on dose-volume histograms from Monte Carlo dose calculations,” Physics in Medicine and Biology, vol. 45, no. 8, pp. 2151–2161, 2000.
[9]
Y. Huang, R. A. Siochi, and J. E. Bayouth, “Dosimetric properties of a beam quality-matched 6 MV unflattened photon beam,” Journal of Applied Clinical Medical Physics, vol. 13, no. 4, pp. 71–81, 2012.
[10]
L. Wang, E. Yorke, G. Desobry, and C. Chui, “Dosimetric advantage of using 6 MV over 15 MV photons in conformal therapy of lung cancer: monte carlo studies in patient geometries,” Journal of Applied Clinical Medical Physics, vol. 3, no. 1, pp. 51–59, 2002.
[11]
G. Battistoni, S. Muraro, P. R. Sala et al., “The FLUKA code: description and benchmarking,” in Proceedings of the Hadronic Shower Simulation Workshop, M. Albrow and R. Raja, Eds., vol. 896 of AIP Conference Proceeding, pp. 31–49, September 2007.
[12]
A. Ferrari, P. R. Sala, A. Fasso, and J. Ranft, FLUKA: A Multi-particle Transport Code, CERN-2005-10, INFN/TC_05/11, SLAC-R-773, 2005.
[13]
V. Vlachoudis, “FLAIR: a powerful but user friendly graphical interface for FLUKA,” in Proceedings of the International Conference on Mathematics, Computational Methods Reactor Physics (M&C '09), Saratoga Springs, NY, USA, 2009.
[14]
R. Brun and F. Rademakers, “ROOT—an object oriented data analysis framework,” in Proceedings of the AIHENP Workshop on Nuclear Instruments and Methods in Physics Research A, pp. 81–86, Lausanne, Switzerland, September 1996, http://root.cern.ch/.
[15]
J. Venselaar, H. Welleweerd, and B. Mijnheer, “Tolerances for the accuracy of photon beam dose calculations of treatment planning systems,” Radiotherapy and Oncology, vol. 60, no. 2, pp. 191–201, 2001.
[16]
C. L. H. Siantar, R. S. Walling, T. P. Daly et al., “Description and dosimetric verification of the PEREGRINE Monte Carlo dose calculation system for photon beams incident on a water phantom,” Medical Physics, vol. 28, no. 7, pp. 1322–1337, 2001.
