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Validation of GATE Monte Carlo code for simulation of proton therapy using National Institute of Standards and Technology library data

Published online by Cambridge University Press:  05 November 2018

Shiva Zarifi ,
Hadi Taleshi Ahangari ,
Sayyed Bijan Jia  and
Mohammad Ali Tajik-Mansoury
Shiva Zarifi
Affiliation:
Department of Medical Physics, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran
Hadi Taleshi Ahangari*
Affiliation:
Department of Medical Physics, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran
Sayyed Bijan Jia
Affiliation:
Department of Physics, University of Bojnord, Bojnord, Iran
Mohammad Ali Tajik-Mansoury
Affiliation:
Department of Medical Physics, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran
*
Author for correspondence: Hadi Taleshi Ahangari, Tel: +98 9127101772. E-mail: [email protected]
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Abstract

Aim

To validate the Geant4 Application for Tomographic Emission (GATE) Monte Carlo simulation code by calculating the proton beam range in the therapeutic energy range.

Materials and methods

In this study, the GATE code which is based on Geant4 was used for simulation. The proton beams in the therapeutic energy range (5–250 MeV) were simulated in a water medium, and then compared with the data from National Institute of Standards and Technology (NIST) in order to investigate the accuracy of different physics list available in the GATE code. In addition, the optimal value of SetCut was assessed.

Results

In all energy ranges, the QBBC physics had a greater deviation in the ranges relative to the NIST data. With respect to the range calculation accuracy, the QGSP_BIC_EMY and QGSP_BERT_HP_EMY physics were in the range of statistical uncertainty; however, QGSP_BIC_EMY produced better results using the least squares. Based on an investigation into the range calculation precision and simulation efficiency, the optimal SetCut was set at 0·1 mm.

Findings

Based on an investigation into the range calculation precision and simulation yield, the QGSP_BIC_EMY physics and the optimal SetCut was recommended to be 0·1 mm.

Keywords

Bragg peakGATE codepencil beamphysics listproton range
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 18 , Issue 1 , March 2019 , pp. 38 - 45
Copyright
© Cambridge University Press 2018 

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Footnotes

Cite this article: Zarifi S, Taleshi Ahangari H, Jia SB, Tajik-Mansoury MA. (2019) Validation of GATE Monte Carlo code for simulation of proton therapy using National Institute of Standards and Technology library data. Journal of Radiotherapy in Practice18: 38–45. doi: 10.1017/S1460396918000493

References

1. Haberer, T. (ed). Advances in charged particle therapy. AIP Conference Proceedings. Berkeley, USA: AIP, 2002.Google Scholar
2. Owadally, W, Staffurth, J. Principles of cancer treatment by radiotherapy. Surgery 2015; 33 (3): 127–130.Google Scholar
3. Walton, E L. Positive perspectives from proton therapy. Biomed J 2015; 38 (5): 361–364.Google Scholar
4. Wilson, R R. Radiological use of fast protons. Radiology 1946; 47 (5): 487–191.Google Scholar
5. Kraft, G. Tumor therapy with heavy charged particles. Progr Particle Nucl Phys 2000; 45: S473–S544.Google Scholar
6. Amaldi, U, Kraft, G. Radiotherapy with beams of carbon ions. Reports Prog Phys 2005; 68 (8): 1861–1882.Google Scholar
7. Paganetti, H, Jiang, H, Parodi, K et al. Clinical implementation of full Monte Carlo dose calculation in proton beam therapy. Phys Med Biol 2008; 53 (17): 4825–4853.Google Scholar
8. Paganetti, H. Proton Therapy Physics. Boca Raton, FL: CRC Press, 2016.Google Scholar
9. Agostinelli, S, Allison, J, Amako, K A et al. GEANT4—a simulation toolkit. Nucl Instr Methods Phys Res Sect A Accelerat Spectromet Detect Associat Equip 2003; 506 (3): 250–303.Google Scholar
10. Waters, L S. MCNPX User’s Manual. Los Alamos, NM: Los Alamos National Laboratory 2002.Google Scholar
11. Ferrari, A, Sala, P, Fasso, A et al FLUKA: a multi-particle transport code. CERN 2005-10 (2005). INFN/TC_05/11, SLAC.Google Scholar
12. Hall, D C, Makarova, A, Paganetti, H et al. Validation of nuclear models in Geant4 using the dose distribution of a 177 MeV proton pencil beam. Phys Med Biol 2015; 61 (1): N1–N10.Google Scholar
13. Assie, K, Breton, V, Buvat, I et al. Monte Carlo simulation in PET and SPECT instrumentation using GATE. Nucl Instr Methods Phys Res Sect A Accelerat Spectromet Detect Associat Equip 2004; 527 (1): 180–189.Google Scholar
14. Jan, S, Santin, G, Strul, D et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol 2004; 49 (19): 4543–4561.Google Scholar
15. Santin, G, Strul, D, Lazaro, D et al (ed). GATE, a Geant4-based simulation platform for PET integrating movement and time management. Nuclear Science Symposium Conference Record, 2002 IEEE; 2002: IEEE.Google Scholar
16. Allison, J, Amako, K, Apostolakis, J et al. Geant4 developments and applications. IEEE Trans Nucl Sci 2006; 53 (1): 270–278.Google Scholar
17. Jan, S, Benoit, D, Becheva, E et al. GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys Med Biol 2011; 56 (4): 881.Google Scholar
18. Ivantchenko, A V, Ivanchenko, V N, Molina, J-M Q et al. Geant4 hadronic physics for space radiation environment. Int J Radiat Biol 2012; 88 (1–2): 171–175.Google Scholar
19. Geant4-Collaboration. Geant4—A Simulation Toolkit—Guide for Physics Lists. CERN, geant4. 2017; 10·4.Google Scholar
20. Jia, S B, Hadizadeh, M H, Mowlavi, A A et al. Evaluation of energy deposition and secondary particle production in proton therapy of brain using a slab head phantom. Rep Pract Oncol Radiother 2014; 19 (6): 376–384.Google Scholar
21. Jia, S B, Romano, F, Cirrone, G A et al. Designing a range modulator wheel to spread-out the Bragg peak for a passive proton therapy facility. Nucl Instr Methods Phys Res Sect A Accelerat Spectromet Detect Associat Equip 2016; 806: 101–108.Google Scholar
22. Paganetti, H. Range uncertainties in proton therapy and the role of Monte Carlo simulations. Phys Med Biol 2012; 57 (11): R99.Google Scholar
23. Berger, M, Coursey, J, Zucker, M et al Stopping-power and range tables for electrons, protons, and helium ions, 2005. http://physicsnistgov. 2015. Accessed on March 2018.Google Scholar
24. Grevillot, L, Frisson, T, Zahra, N et al. Optimization of GEANT4 settings for proton pencil beam scanning simulations using GATE. Nucl Instr Methods Phys Res Sect B Beam Interact With Mater Atoms 2010; 268 (20): 3295–3305.Google Scholar
25. Gottschalk, B. Passive beam spreading in proton radiation therapy, unpublished book, 2004.Google Scholar
26. Bozkurt, A. (ed). Monte Carlo calculation of proton stopping power and ranges in water for therapeutic energies. EPJ Web of Conferences. EDP Sciences. Les Ulis, France, 2017.Google Scholar
27. Zahra, N, Frisson, T, Grevillot, L et al. Influence of Geant4 parameters on dose distribution and computation time for carbon ion therapy simulation. Phys Medica Eur. J Med Phys 2010; 26 (4): 202–208.Google Scholar