Computing Efficiency Improvement in Monte Carlo Simulation of a 12 MV Photon Beam Medical LINAC

DOI: 10.4236/wjnst.2013.31003   PDF   HTML   XML   4,291 Downloads   7,229 Views   Citations


Variance reduction techniques (VRTs) have been tremendously successful when applied to Monte Carlo radiation transport codes for which the computation time constitutes an important and a problematic parameter. In fact, many Monte Carlo calculations absolutely require variance reduction methods to achieve practical computation times. The MCNPX code has a fairly rich set of variance reduction techniques; the most known are transport cutoffs, interaction forcing, Bremsstrahlung splitting and Russian roulette. Also, the use of a phase space seems to be appropriate to reduce enormously the computing time. This work deals with the use of VRTs provided by MCNPX code for the simulation of a clinical linear electron accelerator (LINAC). Differences between various sets of VRTs are investigated. Combination between VRTs and PS is also analyzed during this study. Analysis showed that the use of VRTs and PS improve the simulation efficiency by a factor greater than 700. Finally, experimental curves of depth-dose and dose profile performed in a homogeneous water phantom are compared to dose distributions computed by use of MCNPX Monte Carlo code.

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M. Zoubair, T. Bardouni, O. Allaoui, Y. Boulaich, B. Bakkari, C. Younoussi, H. Boukhal and E. Chakir, "Computing Efficiency Improvement in Monte Carlo Simulation of a 12 MV Photon Beam Medical LINAC," World Journal of Nuclear Science and Technology, Vol. 3 No. 1, 2013, pp. 14-21. doi: 10.4236/wjnst.2013.31003.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] B. Habib, B. Poumarede, F. Tola and J. Barthe, “Evaluation of PENFAST a Fast Monte Carlo Code for Dose Calculations in Photon and Electron Radiotherapy Treatment Planning,” Physica Medica, Vol. 26, No. 1, 2010, pp. 1725. doi:10.1016/j.ejmp.2009.03.002
[2] D. Sheikh-Bagheri and D. W. O. Rogers, “Sensitivity of Megavoltage Photon Beam Monte Carlo Simulations to Electron Beam and Other Parameters,” Med Phys, Vol. 29, No. 3, 2002, p. 379.
[3] B. Juste, R. Miró, S. Gallardo, A. Santos and G. Verdú, “Considerations of MCNP Monte Carlo Code to Be Used as a Radiotherapy Treatment Planning Tool,” 27th Annual Conference of the Engineering in Medicine and Biology, Shanghai, 1-4 September 2005, pp. 2828-2831.
[4] I. Kawrakow and M. Fippel, “Investigation of Variance Reduction Techniques for Monte Carlo Photon Dose Calculation Using XVMC,” Physics in Medicine and Biology, Vol. 45, No. 8, 2000, pp. 2163-2183. doi:10.1088/0031-9155/45/8/308
[5] 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, 2005, pp. 469477. doi:10.1016/j.apradiso.2004.07.008
[6] D. B. Pelowitz, “MCNPXTM User’s Manual Version 2.5.0,” 2005.
[7] L. Blazy, D. Baltes, J. M. Bordy, D. Cutarella, F. Delaunay and J. Gouriou, “Comparison of PENELOPE Monte Carlo Dose Calculations with Fricke Dosimeter and Ionization Chamber Measurements in Inhomogeneous Phantoms (18 MeV Electron and 12 MV Photon Beams),” Physics in Medicine and Biology, Vol. 51, No. 22, 2006, pp. 5951-5965. doi:10.1088/0031-9155/51/22/016
[8] K. Jabbari, A. Sarfehnia, E. Podgorsak and J. P. Seuntjens, “Monte Carlo Feasibility Study of Orthogonal Bremsstrahlung Beams for Improved Radiation Therapy Imaging,” Physics in Medicine and Biology, Vol. 52, No. 4, 2007, p. 1171. doi:10.1088/0031-9155/52/4/021
[9] A. Ma, J. Awotwi-Pratt, A. Alghamdi, A. Alfuraih and N. M. Spyrou, “Monte Carlo Study of Photoneutron Production in the Varian Clinac 2100C Linac,” Radioanalytical and Nuclear Chemistry, Vol. 276, No. 1, 2008, pp. 119123.
[10] S. G. Pareja, M. Vilches and A. M. Lallena, “Ant Colony Method to Control Variance Reduction Techniques in the Monte Carlo Simulation of Clinical Electron Linear Accelerators,” Nuclear Instruments and Methods in Physics Research A, Vol. 580, No. 1, 2007, pp. 510-513. doi:10.1016/j.nima.2007.05.217
[11] D. W. O. Rogers, B. A. Faddegon, G. X. Ding, C. M. Ma, J. Wie and T. R. Mackie, “BEAM: A Monte Carlo Code to Simulate Radiotherapy Treatment Units,” Medical Physics, Vol. 22, No. 5, 1995, pp. 503-524. doi:10.1118/1.597552
[12] N. Reynaert, S. van der Marck and D. Schaart, “Monte Carlo Treatment Planning—An Introduction, Nederlandse Commissie Voor Stralingsdosimetrie,” Report 16 of the Netherlands Commission on Radiation Dosimetry Netherlands, Commission on Radiation Dosimetry Subcommission Monte Carlo Treatment Planning.
[13] X-5 Monte Carlo Team MCNP, “A General Monte Carlo N-Particle Transport Code,” Version 5, 2003.
[14] J. K. Shultis and R. E. Faw, “An MCNP Primer,” Kansas State University, Manhattan, 2011.

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