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Monte Carlo calculations of an Elekta Precise SL-25 photon beam model

Published online by Cambridge University Press:  18 August 2015

Fátima Padilla-Cabal ,
Mailyn Pérez-Liva ,
Elier Lara ,
Rodolfo Alfonso  and
Neivy Lopez-Pino
Fátima Padilla-Cabal
Affiliation:
Nuclear Physics Department, Instituto Superior de Tecnologías y Ciencias Aplicadas, La Habana, Cuba
Mailyn Pérez-Liva*
Affiliation:
Grupo de Física Nuclear, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid, Spain
Elier Lara
Affiliation:
Department of Radiotherapy, Instituto Nacional de Oncología y Radiobiología, La Habana, Cuba
Rodolfo Alfonso
Affiliation:
Nuclear Physics Department, Instituto Superior de Tecnologías y Ciencias Aplicadas, La Habana, Cuba
Neivy Lopez-Pino
Affiliation:
Nuclear Physics Department, Instituto Superior de Tecnologías y Ciencias Aplicadas, La Habana, Cuba
*
Correspondence to: Mailyn Pérez-Liva, Ave Complutense, S/N, Grupo de Física Nuclear, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, 28040 Madrid, Spain. Tel: +34 91 394 4484. Fax: +34 91 394 5193. E-mail: [email protected]
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Abstract

Background

Monte Carlo (MC) simulations have been used extensively for benchmarking photon dose calculations in modern radiotherapy using linear accelerators (linacs). Moreover, a major barrier to widespread clinical implementation of MC dose calculation is the difficulty in characterising the radiation source using data reported from manufacturers.

Purpose

This work aims to develop a generalised full MC histogram source model of an Elekta Precise SL-25 linac (electron exit window, target, flattening filter, monitor chambers and collimators) for 6 MV photon beams used in standard therapies. The inclusion of many different probability processes such as scatter, nuclear reactions, decay, capture cross-sections and more led to more realistic dose calculations in treatment planning and quality assurance.

Materials and methods

Two different codes, MCNPX 2·6 and EGSr-BEAM, were used for the calculation of particle transport, first in the geometry of the internal/external accelerator source, and then followed by tracking the transport and energy deposition in phantom-equivalent tissues. A full phase space file was scored directly above the upper multilayer collimator’s jaws to derive the beam characteristics such as planar fluence, angular distribution and energy spectrum. To check the quality of the generated photon beam, its depth dose curves and cross-beam profiles were calculated and compared with measured data.

Results

In-field dose distributions calculated using the accelerator models were tuned to match measurement data with preliminary calculations performed using the accelerator information provided by the manufacturer. Field sizes of 3×3, 5×5, 10×10, 15×15 and 20×20 cm2 were analysed. Local differences between calculated and measured curve doses beneath 2% were obtained for all the studied field sizes. Higher discrepancies were obtained in the air–water interface, where measurements of dose distributions with the ionisation chamber need to be shifted for the effective point of measurement.

Conclusion

The agreements between MC-calculated and measured dose distributions were excellent for both codes, showing the strength and stability of the proposed model. Beam reconstruction methods as direct input to dose-calculation codes using the recorded histograms can be implemented for more accurate patient dose estimation.

Keywords

Elekta-linacMonte Carlo (MC) simulationsphoton beam model
Type
Technical Note
Information
Journal of Radiotherapy in Practice , Volume 14 , Issue 3 , September 2015 , pp. 311 - 322
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Copyright
© Cambridge University Press 2015 

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References

1.Ahnesjö, A, Aspradakis, M M. Dose calculations for external photon beams in radiotherapy. Phys Med Biol 1999; 44: R99–R155.CrossRefGoogle ScholarPubMed
2.Jabbari, K. Review of fast Monte Carlo codes for dose calculation in radiation therapy treatment planning. J Med Signals Sens 2011; 1: 73–86.CrossRefGoogle ScholarPubMed
3.Beckham, W, Keall, P J, Siebers, J V. A fluence-convolution method to calculate radiation therapy dose distributions that incorporate random set-up error. Phys Med Biol 2002; 47: 3465–3473.CrossRefGoogle ScholarPubMed
4.Fanti, V, Marzeddu, R, Pili, Cet al Dose calculation for radiotherapy treatment planning using Monte Carlo methods on FPGA based hardware. 16th IEEE-NPSS Real Time Conference, 10–15 May, 2009, Beijing, China, 2009, 415–419.CrossRefGoogle Scholar
5.Ma, C-M, Li, J, Pawlicki, T. A Monte Carlo dose calculation tool for radiotherapy treatment planning. Phys Med Biol 2002; 47: 1671–1689.CrossRefGoogle ScholarPubMed
6.Pellowitz, D. MCNPX User’s Manual, Version 2.6.0. Report No. LA CP. Los Alamos National Laboratory, Los Alamos, Nuevo Mexico, USA, 2007.Google Scholar
7.Rogers, D W, Faddegon, B A, Ding, G X, Ma, C M, We, J, Mackie, T R. BEAM: a Monte Carlo code to simulate radiotherapy treatment units. Med Phys 1995; 22: 503–524.CrossRefGoogle Scholar
8.Rogers, D, Walters, B, Kawrakow, I. BEAMnrc Users Manual. Rep. PIRS. NRC, Ottawa, Canada, 2001.Google Scholar
9.Ding, G X. Energy spectra, angular spread, fluence profiles and dose distributions of 6 and 18 MV photon beams: results of Monte Carlo simulations for a Varian 2100EX accelerator. Phys Med Biol 2002; 47: 1025–1046.CrossRefGoogle ScholarPubMed
10.Sheikh-Bagheri, D, Rogers, D. Comparison of measured and Monte Carlo calculated dose distributions from the NRC linac. Med Phys 2000; 10: 2256–2266.CrossRefGoogle Scholar
11.Cho, S, Vassiliev, O, Lee, S, Liu, H. Reference photon dosimetry data and reference phase space data for the 6 MV photon beam from Varian Clinac 2100 series linear accelerators. Med Phys 2005; 32: 137–148.CrossRefGoogle ScholarPubMed
12.Van de Walle, J, Martens, C. Monte Carlo model of the Elekta SLiplus accelerator: validation of a new MLC component module in BEAM for a 6 MV beam. Phys Med Biol 2003; 48: 371–385.CrossRefGoogle ScholarPubMed
13.Deng, J, Jiang, S, Kapur, A. Photon beam characterization and modelling for Monte Carlo treatment planning. Phys Med Biol 2000; 45: 411–427.CrossRefGoogle ScholarPubMed
14.Bush, K, Popescu, I, Zavgorodni, S. A technique for generating phase-space-based Monte Carlo beamlets in radiotherapy applications. Phys Med Biol 2008; 53: N337–N347.CrossRefGoogle ScholarPubMed
15.Chvetsov, A, Sandison, G. Reconstruction of electron spectra using singular component decomposition. Med Phys 2002; 29 (4): 578–591.CrossRefGoogle ScholarPubMed
16.Herranz, E, Herraiz, J L, Cal-Gonzalez, J, Corzo, P M G, Guerra, P, Udias, J M. Iterative reconstruction of whole accelerator phase spaces for intraoperative radiation therapy (IORT) from measured dose data. Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) 2011, IEEE, 23--29 October, 2011, Valencia, Spain, 2644–2646.CrossRefGoogle Scholar
17.Herranz, E, Herraiz, J, Guerra, Pet al. Iterative determination of clinical beam phase space from dose measurements. Int J Radiat Oncol Biol Phys 2012; 84: S869.CrossRefGoogle Scholar
18.Tzedakis, A, Damilakis, J, Mazonakis, M. Influence of initial electron beam parameters on Monte Carlo calculated absorbed dose distributions for radiotherapy photon beams. Med Phys 2004; 31: 907–913.CrossRefGoogle ScholarPubMed
19.ICRU. Tissue substitutes in radiation dosimetry and measurement. Report 44 s (Bethesda, USA). Comm Radiat Units Meas 1989.Google Scholar
20.Mesbahi, A, Reilly, A, Thwaites, D. Development and commissioning of a Monte Carlo photon beam model for Varian Clinac 2100EX linear accelerator. Appl Radiat Isot 2006; 64: 656–662.CrossRefGoogle ScholarPubMed
21.Verhaegen, F, Seuntjens, J. Monte Carlo modelling of external radiotherapy photon beams. Phys Med Biol 2003; 48: 107–164.CrossRefGoogle ScholarPubMed
22.Kim, H, Han, S, Kim, J, Kim, B. Monte Carlo simulation of the photon beam characteristics from medical linear accelerators. Radiat Prot Dosimetry 2006; 119 (1–4): 510–513.CrossRefGoogle ScholarPubMed
23.Almond, P R, Biggs, P J, Coursey, B Met al. AAPM’s TG-51 protocol for clinical reference dosimetry of high-energy photon and electron beams. Med Phys 1999; 26: 1847–1870.CrossRefGoogle ScholarPubMed
24.IAEA. Absorbed dose determination in external beam radiotherapy: an international code of practice for dosimetry. TRS-398 vol V.10A, IAEA, Vienna, Austria, 2001.Google Scholar
25.Berger, M J, Hubbell, J H. XCOM: photon cross sections on a personal computer. Report NBSIR 87-3597, National Bureau of Standards, Gaithersburg, MD, 1987.CrossRefGoogle Scholar
26.Mesbahi, A. Dosimetric characteristics of unflattened 6 MV photon beams of a clinical linear accelerator: a Monte Carlo study. Appl Radiat Isot 2007; 65: 1029–1036.CrossRefGoogle ScholarPubMed
27.van der Zee, W, Welleweerd, J. Calculating photon beam characteristics with Monte Carlo techniques. Med Phys 1999; 26 (9): 1883–1892.CrossRefGoogle ScholarPubMed