Monte Carlo Simulation-Based BEAMnrc Code of a 6 MV Photon Beam Produced by a Linear Accelerator (LINAC)

R. Sapundani, R. Ekawati, K. M. Wibowo


In radiotherapy, high energy ionizing radiation, such as X-rays, gamma rays and electron beams,is used. The dose in the tissue is often approached with the dose in the medium of the body which is 80 % of human soft tissue. It is often difficult to determine the dose because the interaction of materials in a medium is very random. Measurement is also quite difficult because there are almost no detectors that are tissue equivalent. Measurement using an ion chamber requires a lot of correction to obtain a dose in the tissue, which is done using phantom and not directly in humans. This research aimed to compare the absorbed dose between modelling using Monte Carlo simulation and experiments.  The simulation of dose distribution produced by a 6 MV medical linear accelerator has been performed using BEAMnrc code running on Linux-based 2 processor system arranged in parallel.BEAMnrc was used to model and simulate the linac head with an SSD of 100 cm and Field size of10x10 cm2. A phase-space file is obtained as input to a DOSXYnrc code to produce Percent Depth Dose (PDD) in water and polymethyl methacrylate (PMMA) phantoms. New particles formed (electrons: 0.2 %, photon: 0.17 %; and positron: 0.08 %) were far from the contamination threshold of 2 %. The range of the correction factor of the depth of a maximum dose compared to the experimental data was 0.04-0.15.


Monte Carlo simulation; phase-space file; BEAMnrc code; DOSXYZnrc code

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M. Machichi, Y. Oulhouq, A. Rrhioua et al., Mater. Today Proc. 13 (2019) 982.

D. W. O. Rogers, B. Walters and I. Kawrakow, BEAMnrc Users Manual, NRCC Report PIRS-0509(A)revL, Canada (2021).

S. Gholampourkashi, J. E. Cygler, J. Belec et al., E. Heath, J. Appl. Clin. Med. Phys. 20 (2019) 55.

M. Bencheikh, A. Maghnouj, J. Tajmouati et al., Phys. Part. Nucl. Lett. 14 (2017) 780.

J. EL Bakkali and T. EL Bardouni, J. King Saud Univ. - Sci. 29 (2017) 106.

R. Shende, G. Gupta, G. Patel et al., Int. J. Med. Physics, Clin. Eng. Radiat. Oncol. 05 (2016) 51.

R. N. Sruti, M. M. Islam, M. M. Rana et al., Nucl. Sci. Appl. 24 (2015) 29.

S. Didi, A. Moussa, T. Yahya et al., J. Med. Phys. 40 (2015) 136.

B. Kadman, N. Chawapun, S. Ua-Apisitwong et al., J. Phys.: Conf. Ser. 694 (2016) 1.

K. R. Mani, M. A. Bhuiyan, M. S. Rahman et al., Polish J. Med. Phys. Eng. 24 (2018) 79.

M. Mohammed, T. El Bardouni, E. Chakir et al., J. King Saud Univ. - Sci. 30 (2018) 537.

A. Kajaria, N. Sharma, S. Sharma et al., Int. J. Appl. Eng. Res. 11 (2016) 8185.

S. Yani, M. F. Rhani, R. C. X. Soh et al., Int. J. Radiat. Res. 15 (2017) 275.

K. Sachse and F. C. P. du Plessis, Phys. Medica 41 (2017) 3.

K. Hadad, M. Saeedi-Moghadam and B. Zeinali-Rafsanjani, Technol. Health Care 25 (2017) 29.

A. Ghila, S. Steciw, B. G. Fallone et al., Med. Phys. 44 (2017) 4804.

B. R. B. Walters, Med. Phys. 42 (2015) 5817.

J. E. Morales, M. Butson, R. Hill et al., Phys. Eng. Sci. Med. 43 (2020) 609.

S. Stathakis, F. Balbi, A. T. Chronopoulos et al., J. BUON. 21 (2016) 252.

M. Nasir, D. Pratama, C. Anam et al., J. Phys: Conf. Ser. 694 (2016) 1.

L. T. Campos, L. A. Magalhães and C. E. V. de Almeida, J. Biomed. Phys. Eng. 9 (2019) 259.

S. Bao, F. D. Weitendorf, A. J. Plassard et al., Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine, Proceedings, Medical Imaging, Imaging Informatics for Healthcare, Research, and Applications 10138 (2017) 101380.


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