DOI QR코드

DOI QR Code

Bragg-curve simulation of carbon-ion beams for particle-therapy applications: A study with the GEANT4 toolkit

  • Hamad, Morad Kh. (Physics Department, King Fahd University of Petroleum & Minerals)
  • Received : 2020.09.21
  • Accepted : 2021.02.11
  • Published : 2021.08.25

Abstract

We used the GEANT4 Monte Carlo MC Toolkit to simulate carbon ion beams incident on water, tissue, and bone, taking into account nuclear fragmentation reactions. Upon increasing the energy of the primary beam, the position of the Bragg-Peak transfers to a location deeper inside the phantom. For different materials, the peak is located at a shallower depth along the beam direction and becomes sharper with increasing electron density NZ. Subsequently, the generated depth dose of the Bragg curve is then benchmarked with experimental data from GSI in Germany. The results exhibit a reasonable correlation with GSI experimental data with an accuracy of between 0.02 and 0.08 cm, thus establishing the basis to adopt MC in heavy-ion treatment planning. The Kolmogorov-Smirnov K-S test further ascertained from a statistical point of view that the simulation data matched the experimentally measured data very well. The two-dimensional isodose contours at the entrance were compared to those around the peak position and in the tail region beyond the peak, showing that bone produces more dose, in comparison to both water and tissue, due to secondary doses. In the water, the results show that the maximum energy deposited per fragment is mainly attributed to secondary carbon ions, followed by secondary boron and beryllium. Furthermore, the number of protons produced is the highest, thus making the maximum contribution to the total dose deposition in the tail region. Finally, the associated spectra of neutrons and photons were analyzed. The mean neutron energy value was found to be 16.29 MeV, and 1.03 MeV for the secondary gamma. However, the neutron dose was found to be negligible as compared to the total dose due to their longer range.

Keywords

Acknowledgement

The author would like to acknowledge the support provided by the Deanship of Research at King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia. Special thanks are due to Dr. Dieter Schardt (GSI) for providing us with the tables of experimental data on depth - dose distributions. Great gratitude is extended to Prof. Saed Dababneh who is no longer with us. His valuable advices and kind notices are deeply thanked.

References

  1. J. Soltani-Nabipour, A. Khorshidi, F. Shojai, K. Khorami, Evaluation of dose distribution from 12C ion in radiation therapy by FLUKA code, Nuclear Engineering And Technology 52 (2020) 2410-2414, https://doi.org/10.1016/j.net.2020.03.010.
  2. O. Jakel, Medical physics aspects of particle therapy, Radiat. Protect. Dosim. 137 (2009) 156-166, https://doi.org/10.1093/rpd/ncp192.
  3. O. Jakel, D. Schulz-Ertner, C. Karger, A. Nikoghosyan, J. Debus, Heavy ion therapy: status and perspectives, Technol. Canc. Res. Treat. 2 (2003) 377-387, https://doi.org/10.1177/153303460300200503.
  4. D. Schulz-Ertner, O. Jakel, W. Schlegel, Radiation therapy with charged particles, Semin. Radiat. Oncol. 16 (2006) 249-259, https://doi.org/10.1016/j.semradonc.2006.04.008.
  5. W. Bragg, R. Kleeman, On the a particles of radium, and their loss of range in passing through various atoms and molecules, The London, Edinburgh, And Dublin Philosophical Magazine And Journal Of Science 10 (1905) 318-340, https://doi.org/10.1080/14786440509463378.
  6. J.S. Nabipour, A. Khorshidi, Spectroscopy and optimizing semiconductor detector data under X and g photons using image processing technique, J. Med. Imag. Radiat. Sci. 49 (2) (2018) 194-200, https://doi.org/10.1016/j.jmir.2018.01.004.
  7. L. Sihver, D. Schardt, T. Kanai, Depth-dose distributions of high-energy carbon, oxygen and neon beams in water, Jpn. J. Appl. Phys. 18 (1998), https://doi.org/10.11323/jjmp1992.18.1_1.
  8. M. Hultqvist, J. Lillhok, L. Lindborg, I. Gudowska, H. Nikjoo, Nanodosimetry in a 12C ion beam using Monte Carlo simulations, Radiat. Meas. 45 (2010) 1238-1241, https://doi.org/10.1016/j.radmeas.2010.05.033.
  9. G. Kraft, Tumor therapy with heavy charged particles, Prog. Part. Nucl. Phys. 45 (2000) S473-S544, https://doi.org/10.1016/s0146-6410(00)00112-5.
  10. G. Kraft, M. Scholz, U. Bechthold, Tumor therapy and track structure, Radiat. Environ. Biophys. 38 (1999) 229-237, https://doi.org/10.1007/s004110050163.
  11. S. Brons, G. Taucher-Scholz, M. Scholz, G. Kraft, A track structure model for simulation of strand breaks in plasmid DNA after heavy ion irradiation, Radiat. Environ. Biophys. 42 (2003) 63-72, https://doi.org/10.1007/s00411-003-0184-9.
  12. P. Azimi, A. Movafeghi, Proton therapy in neurosurgery: a historical review and future perspective based on currently available new generation systems, Int. J. Clin. Neurosci. 3 (2) (2016) 59-80, https://doi.org/10.22037/icnj.v3i2.13324.
  13. S. Malmir, A. Asghar Mowlavi, S. Mohammadi, Enhancement evaluation of energy deposition and secondary particle production in gold nanoparticle aided tumor using proton therapy, Int. J. Canc. Manag. 10 (10) (2017), e10719, https://doi.org/10.5812/ijcm.10719.
  14. A. Khorshidi, Accelerator-based methods in radio-material 99Mo/99mTc production alternatives by Monte Carlo method: the scientific-expedient considerations in nuclear medicine, J. Multiscale Model. (JMM) 11 (1) (2020) 1930001, https://doi.org/10.1142/S1756973719300016.
  15. E. Segre, H. Staub, H. Bethe, et al., Experimental Nuclear Physics, first ed., John Wiley & Sons, New York, 1953.
  16. Jose R. Alonso, Review of ion beam therapy, Present and Future (2000). United States, https://www.osti.gov/servlets/purl/765471.
  17. E.J. Hall, Radiobiology for the Radiologist, fourth ed., Lippincott Williams & Wilkins, 1993.
  18. E. Haettner, Experimental Study on Carbon Ion Fragmentation in Water Using GSI Therapy Beams, KTH Royal Institute of Technology in Stockholm, 2006.
  19. P. Petti, A. Lennox, Hadronic radiotherapy, Annu. Rev. Nucl. Part Sci. 44 (1994) 155-197, https://doi.org/10.1146/annurev.ns.44.120194.001103.
  20. J. Soltani-Nabipour, M. Popovici, G. Cata-Danil, Residual Nuclei Produced by 290 MeV/u 12C ions beam in a liquid water target, Rom. Rep. Phys. 62 (2010) 37-46. http://194.102.58.21/2010_62_01/art04Soltanidoc.pdf. accessed 9 September 2020.
  21. I. Pshenichnov, I. Mishustin, W. Greiner, Neutrons from fragmentation of light nuclei in tissue-like media: a study with the GEANT4 toolkit, Phys. Med. Biol. 50 (2005) 5493-5507, https://doi.org/10.1088/0031-9155/50/23/005.
  22. Geant4 User's Guide. https://geant4.web.cern.ch/support/user_documentation, 2012.
  23. A. Lechner, V. Ivanchenko, J. Knobloch, Validation of recent Geant4 physics models for application in carbon ion therapy, Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 268 (2010) 2343-2354, https://doi.org/10.1016/j.nimb.2010.04.008.
  24. Dosimetry and Medical Radiation Physics Section, Absorbed Dose Determination in External Beam Radiotherapy, The International Atomic Energy Agency, Vienna, 2000. http://www-pub.iaea.org/mtcd/publications/pdf/trs398_scr.pdf. accessed 9 September 2020.
  25. S. Dababneh, E. Al-Nemri, J. Sharaf, Application of Geant4 in routine close geometry gamma spectroscopy for environmental samples, J. Environ. Radioact. 134 (2014) 27-34, https://doi.org/10.1016/j.jenvrad.2014.02.019.
  26. R Development Core Team, R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
  27. A. Cuadra-Sanchez, J. Aracil, Finding the optimal Aggregation period, Traffic Anomaly Detection (2015) 11-27, https://doi.org/10.1016/b978-1-78548-012-6.50002-3.
  28. E. Mocanu, P. Nguyen, M. Gibescu, Deep learning for power system data analysis, Big Data Application In Power Systems (2018) 125-158, https://doi.org/10.1016/b978-0-12-811968-6.00007-3.
  29. G. Christodoulakis, S. Satchel, The validity of credit risk model validation methods, The Analytics Of Risk Model Validation (2008) 27-43, https://doi.org/10.1016/b978-075068158-2.50006-8.
  30. R. Woods, Validation of registration accuracy, Handbook Of Medical Image Processing And Analysis (2009) 569-575, https://doi.org/10.1016/b978-012373904-9.50043-x.
  31. J. Blackledge, Statistical modelling and analysis, Digital Image Processing (2005) 512-540, https://doi.org/10.1533/9780857099464.4.512.
  32. M. Hultqvist, I. Gudowska, Secondary doses delivered to an anthropomorphic male phantom under prostate irradiation with proton and carbon ion beams, Radiat. Meas. 45 (2010) 1410-1413, https://doi.org/10.1016/j.radmeas.2010.05.020.
  33. A. Khorshidi, Neutron activator design for 99Mo production yield estimation via lead and water moderators in transmutation's analysis, Instrum. Exp. Tech. 61 (2) (2018) 198-204, https://doi.org/10.1134/S002044121802015X.
  34. A. Khorshidi, Molybdenum-99 production via lead and bismuth moderators and milli-structure-98Mo samples by the indirect production technique using the Monte Carlo method, Phys. Usp. 62 (9) (2019) 931-940, https://doi.org/10.3367/UFNe.2018.09.038441.
  35. M. Ashoor, A. Khorshidi, L. Sarkhosh, Appraisal of new density coefficient on integrated-nanoparticles concrete in nuclear protection, Kerntechnik 85 (1) (2020) 9-14, https://doi.org/10.3139/124.190016.
  36. G. Knoll, Radiation Detection and Measurement, third ed., John Wiley & Sons, New York, 1999, pp. 31-32.

Cited by

  1. Recent advances in radiation therapy and photodynamic therapy vol.8, pp.4, 2021, https://doi.org/10.1063/5.0060424