Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

A comprehensive GATE Monte Carlo model for a double scattering proton treatment nozzle

Document Type : Research Paper

Authors

E. Piruzan 1
N. Vosoughi 1
H. Mahani 2

1 Nuclear Engineering Group, Department of Energy Engineering, Sharif University of Technology,, P.O.Box: 11155-1639, Tehran - Iran

2 Radiation Application Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box: 1339-14155, Tehran - Iran

https://doi.org/10.24200/nst.2022.1347
Abstract
Proton beam therapy (PBT) is a modern radiotherapy technique characterized by superior target coverage compared to conventional modalities. In this work, a comprehensive GATE Monte Carlo model was developed and then validated for a double scattering proton treatment nozzle. To this aim, a double scattering treatment nozzle was modeled in the GATE toolkit. Proton beam flatness and its symmetry, secondary neutron effective dose, and dosimetric performance were characterized. A proton beam flatness of 98.6% was observed downstream of the aperture for a 7×7 cm2 field size. The beam flatness deteriorates at the edge of the treatment field for the single scattering model while it remains approximately constant for the double scattering one. Compared to the single scattering delivery, the second scattering model results in a 1.3 times increase in neutron dose for the nickel as the optimal collimator/aperture material. Furthermore, a flat beam modulation width of 3.50 cm is formed with a distal edge at 7.86 cm in water using the GATE and MCNPX codes. The GATE model agreed with the MCNPX results. The results show that the constructed GATE model results in a fast and accurate simulation of passive scattering PBT.

Highlights

1. R.R. Wilson, Radiological use of fast protons, Radiol., 47, 487-91 (1946).

 

2. H. Paganetti, Proton Therapy Physics, 2nd Ed. New York: Taylor & Francis Group (2019).

 

3. B. Jones, The case for particle therapy, Br. J. Radiol, 79, 24–31 (2006).

 

4. C-M. Charlie Ma, T. Lomax, Proton and Carbon Ion Therapy, 1st Ed. New York: CRC Press, 1-250 (2013).

 

5.             E. Piruzan, N. Vosoughi, H. Mahani, In: IEEE International Symposium on Medical Measurement and Applications (MeMeA), A Fast and Accurate GATE Model for Small Field Scattering Proton Beam Therapy (IEEE, New Jersey, 2020),  1-6 (2020).

 

6. H. Paganetti, et al, Clinical implementation of full Monte Carlo dose calculation in proton beam therapy, Phys. Med. Biol, 53, 4825–53 (2008).

 

7. H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations, Phys. Med. Biol, 57, R99 –R117 (2012).

 

8.             P. Andreo, Monte Carlo simulations in radiotherapy dosimetry, Radiat. Oncol, 27, 121 (2018).

 

9. S. Agostinelli, et al, GEANT4—a simulation toolkit, Nucl. Instrum. Meth. Phys, B. 506, 250–303 (2003).

 

10.          L.S. Waters, MCNPX User’s Manual. Los Alamos, NM: Los Alamos National Laboratory, (2002).

 

11.          A. Ferrari, et al, FLUKA: a multi-particle transport code, CERN, (2005).

 

12.          B. Faddegon, et al, The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research, Phys. Med, 72, 114-121 (2020).

 

13.          S. Jan, et al, GATE – Geant4 applications for tomographic emission: a simulation toolkit for PET and SPECT, Phys. Med. Biol, 49, 4543-61 (2004).

 

14. L. Grevillot, et al, Optimization of GEANT4 settings for proton pencil beam scanning simulations using GATE, Nucl. Instrum. Meth, B. 268, 3295-3305 (2010).

 

15.          H. Mahani, et al, Spinning slithole collimation for high-sensitivity small animal SPECT: Design and assessment using GATE simulation, Phys. Med, 40, 42-50 (2017).

 

16. K. Assie, et al, Monte Carlo simulation in PET and SPECT instrumentation using GATE, Nucl. Instrum. Meth, A. 527, 180–189 (2004).

 

17. S. Jan, et. al, GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy, Phys. Med. Biol, 56, 881–901 (2011).

 

18. D. Sarrut, et al, A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications, Med. Phys, 41, 06430 (2014).

 

19.          C. Robert, et al, PET-based dose delivery verification in proton therapy: a GATE based simulation study of five PET system designs in clinical conditions, Phys. Med. Biol, 7, 6867-85 (2013).

 

20.          L. Grevillot, et al, Monte Carlo pencil beam scanning model for proton treatment plan simulation using GATE/GEANT4, Phys. Med. Biol, 21, 5203-19 (2011).

 

21.          L. Grevillot, et al, GATE as a GEANT4-based Monte Carlo platform for the evaluation of proton pencil beam scanning treatment plans, Phys Med Biol, 57, 4223-44 (2012).

 

22. F. Padilla-Cabal, et al, Benchmarking a GATE/Geant4 Monte Carlo model for proton beams in magnetic fields, Med. Phys, 47, 223-233 (2020).

 

23. L. Grevillot, et al, Technical Note: GATE-RTion: a GATE/Geant4 release for clinical applications in scanned ion beam therapy, Med. Phys, 48, 3675-81 (2020).

 

24.          D. Jette, W. Chen, Creating a spread-out Bragg peak in prton beams, Phys. Med. Biol, 56, 131-8 (2011).

 

25. T. Bortfeld, W. Schlegel, An analytical approximation of depth-dose distributions for therapeutic prootn beams, Phys. Med. Biol, 41, 1331-9 (1996).

 

26.          S. Zarifi, et al, Validation of GATE Monte Carlo code for simulation of proton therapy using National Institute of Standards and Technology library data, J. Radiother. Pract, 18, 38-45 (2018).

 

27.          S. Zarifi, et al, Bragg peak characteristics of proton beams within therapeutic energy range and comparison of stopping power using the GATE Monte Carlo simulation and the NIST data, J. Radiother Pract, 12, 173-81 (2019).

 

28. C. Robert, et al, Distributions of secondary particles in proton and carbon-ion therapy: a comparison between GATE/Geant4 and FLUKA Monte Carlo codes, Phys. Med. Biol, 58, 2879-99 (2013).

 

29. D.J. Brenner, et al, Reduction of the secondary neutron dose in passively scattered proton radiotherapy, using an optimized pre-collimator/collimator, Phys. Med. Biol, 54, 6065–78 (2009).

 

30. Ch. Lee, et al, Monte Carlo simulation of secondary neutron dose for scanning proton therapy using FLUKA, Plos One, 12, 1-12 (2017).

 

31. U. Titt, B. Bednarz, H. Paganetti, Comparison of MCNPX and Geant4 proton energy deposition predictions for clinical use, Phys. Med. Biol, 57, 6381–93 (2012).

 

32.          Y. Zheng, et al, Monte Carlo simulation of the neutron spectral fluence and dose equivalent for use in shielding a proton therapy vault, Phys. Med. Biol, 54, 6943-57 (2009).

Keywords

Proton therapy
Double scattering
GATE
Monte Carlo
MCNPX
1. R.R. Wilson, Radiological use of fast protons, Radiol., 47, 487-91 (1946).
 
2. H. Paganetti, Proton Therapy Physics, 2nd Ed. New York: Taylor & Francis Group (2019).
 
3. B. Jones, The case for particle therapy, Br. J. Radiol, 79, 24–31 (2006).
 
4. C-M. Charlie Ma, T. Lomax, Proton and Carbon Ion Therapy, 1st Ed. New York: CRC Press, 1-250 (2013).
 
5.             E. Piruzan, N. Vosoughi, H. Mahani, In: IEEE International Symposium on Medical Measurement and Applications (MeMeA), A Fast and Accurate GATE Model for Small Field Scattering Proton Beam Therapy (IEEE, New Jersey, 2020),  1-6 (2020).
 
6. H. Paganetti, et al, Clinical implementation of full Monte Carlo dose calculation in proton beam therapy, Phys. Med. Biol, 53, 4825–53 (2008).
 
7. H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations, Phys. Med. Biol, 57, R99 –R117 (2012).
 
8.             P. Andreo, Monte Carlo simulations in radiotherapy dosimetry, Radiat. Oncol, 27, 121 (2018).
 
9. S. Agostinelli, et al, GEANT4—a simulation toolkit, Nucl. Instrum. Meth. Phys, B. 506, 250–303 (2003).
 
10.          L.S. Waters, MCNPX User’s Manual. Los Alamos, NM: Los Alamos National Laboratory, (2002).
 
11.          A. Ferrari, et al, FLUKA: a multi-particle transport code, CERN, (2005).
 
12.          B. Faddegon, et al, The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research, Phys. Med, 72, 114-121 (2020).
 
13.          S. Jan, et al, GATE – Geant4 applications for tomographic emission: a simulation toolkit for PET and SPECT, Phys. Med. Biol, 49, 4543-61 (2004).
 
14. L. Grevillot, et al, Optimization of GEANT4 settings for proton pencil beam scanning simulations using GATE, Nucl. Instrum. Meth, B. 268, 3295-3305 (2010).
 
15.          H. Mahani, et al, Spinning slithole collimation for high-sensitivity small animal SPECT: Design and assessment using GATE simulation, Phys. Med, 40, 42-50 (2017).
 
16. K. Assie, et al, Monte Carlo simulation in PET and SPECT instrumentation using GATE, Nucl. Instrum. Meth, A. 527, 180–189 (2004).
 
17. S. Jan, et. al, GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy, Phys. Med. Biol, 56, 881–901 (2011).
 
18. D. Sarrut, et al, A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications, Med. Phys, 41, 06430 (2014).
 
19.          C. Robert, et al, PET-based dose delivery verification in proton therapy: a GATE based simulation study of five PET system designs in clinical conditions, Phys. Med. Biol, 7, 6867-85 (2013).
 
20.          L. Grevillot, et al, Monte Carlo pencil beam scanning model for proton treatment plan simulation using GATE/GEANT4, Phys. Med. Biol, 21, 5203-19 (2011).
 
21.          L. Grevillot, et al, GATE as a GEANT4-based Monte Carlo platform for the evaluation of proton pencil beam scanning treatment plans, Phys Med Biol, 57, 4223-44 (2012).
 
22. F. Padilla-Cabal, et al, Benchmarking a GATE/Geant4 Monte Carlo model for proton beams in magnetic fields, Med. Phys, 47, 223-233 (2020).
 
23. L. Grevillot, et al, Technical Note: GATE-RTion: a GATE/Geant4 release for clinical applications in scanned ion beam therapy, Med. Phys, 48, 3675-81 (2020).
 
24.          D. Jette, W. Chen, Creating a spread-out Bragg peak in prton beams, Phys. Med. Biol, 56, 131-8 (2011).
 
25. T. Bortfeld, W. Schlegel, An analytical approximation of depth-dose distributions for therapeutic prootn beams, Phys. Med. Biol, 41, 1331-9 (1996).
 
26.          S. Zarifi, et al, Validation of GATE Monte Carlo code for simulation of proton therapy using National Institute of Standards and Technology library data, J. Radiother. Pract, 18, 38-45 (2018).
 
27.          S. Zarifi, et al, Bragg peak characteristics of proton beams within therapeutic energy range and comparison of stopping power using the GATE Monte Carlo simulation and the NIST data, J. Radiother Pract, 12, 173-81 (2019).
 
28. C. Robert, et al, Distributions of secondary particles in proton and carbon-ion therapy: a comparison between GATE/Geant4 and FLUKA Monte Carlo codes, Phys. Med. Biol, 58, 2879-99 (2013).
 
29. D.J. Brenner, et al, Reduction of the secondary neutron dose in passively scattered proton radiotherapy, using an optimized pre-collimator/collimator, Phys. Med. Biol, 54, 6065–78 (2009).
 
30. Ch. Lee, et al, Monte Carlo simulation of secondary neutron dose for scanning proton therapy using FLUKA, Plos One, 12, 1-12 (2017).
 
31. U. Titt, B. Bednarz, H. Paganetti, Comparison of MCNPX and Geant4 proton energy deposition predictions for clinical use, Phys. Med. Biol, 57, 6381–93 (2012).
 
32.          Y. Zheng, et al, Monte Carlo simulation of the neutron spectral fluence and dose equivalent for use in shielding a proton therapy vault, Phys. Med. Biol, 54, 6943-57 (2009).

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