Skip to main content
Log in

Photon beam softening coefficient determination with slab thickness in small filed size: Monte Carlo study

  • Radiobiology, Ecology and Nuclear Medicine
  • Published:
Physics of Particles and Nuclei Letters Aims and scope Submit manuscript

Abstract

Determination and understanding of photon beam softening using material soften photon beam for clinical usage is important for material study for attenuation and for beam modifier enhancements and linac improvements. Monte Carlo model was used to simulate 6 MeV photon beams produced by Varian Clinac 2100 accelerator with flattening filter thereafter the flattening filter was replaced by a slab of aluminum and copper with different of 0.5, 1, 1.5 and 2 mm. The Monte Carlo geometry was validated by a gamma index acceptance rate of 99% in PDD and 98% in dose profiles, the gamma criteria was 3% for dose difference and 3mm for distance to agreement. The purpose was to investigate the beam softening for small size and beam attenuation as a function of inserted slab thickness of copper and aluminum and also as a function of off-axis distance. For beam softening evaluation, variation amplitude of beam softening coefficient a1 was very high near the beam central axis and decreased with off-axis distance and also it was high for aluminum slab compared to copper slab. For aluminum slab, variation amplitude of beam softening coefficient a1 have a minimum at–0.5 cm–1 and a maximum at 0.5 cm–1 and for copper slab, variation amplitude of beam softening a1 have a minimum at–0.15 cm–1 and a maximum at 0.11 cm–1. Variation amplitude of beam softening coefficient a2 was very high near the beam central axis and decreased with off-axis distance and it was very high for aluminum slab compared to copper slab. For aluminum slab, variation amplitude of beam softening coefficient a2 have a minimum at–0.54 cm–2 and a maximum at 0.44 cm–2 and for copper slab, variation amplitude of beam softening coefficient a2 have a minimum at–0.111 cm–2 and a maximum at 0.0825 cm–2.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. J. El Bakkali and T. El Bedouin, “Validation of Monte Carlo Geant4 code for a 6 MeV varian linac,” J. King Saud Univ. Science (2016).

    Google Scholar 

  2. Y. Tayalati, S. Didi, M. Zerfaoui, and A. Moussa, “Monte Carlo simulation of 6 MeV elekta synergy platform linac photon beam using Gate/Geant4,” J. Med. Phys. (2013); arXiv:13090758.

    Google Scholar 

  3. S. Didi, A. Moussa, Y. Tayalati, and M. Zerfaoui, “Simulation of the 6 MeV elekta synergy platform linac photon beam using Geant4 application for tomographic emission,” J. Med. Phys. 40, 136–143(2015).

    Article  Google Scholar 

  4. D. W. O. Rogers, B. Walters, and I. Kawrakow, “BEAMnrc users manual,” NRCC Report (Natl. Res. Council of Canada, Ottawa, 2013), pp. 12–254.

    Google Scholar 

  5. B. Walters, I. Kawrakow, and D. W. O. Rogers, “DOSXYZnrc users manual,” NRCC Report (Natl. Res. Council of Canada, Ottawa, 2013), pp. 9–103.

    Google Scholar 

  6. D. W. O. Rogers, I. Kawrakow, J. P. Seuntjens, B. Walters, and H. E. Mainegra, “NRC user codes for EGSnrc,” NRCC Report (Natl. Res. Council of Canada, Ottawa, 2013), pp. 6–83.

    Google Scholar 

  7. C. M. Ma and D. W. O. Rogers, “BEAMDP users manual,” NRCC Report (Natl. Res. Council of Canada, Ottawa, 2013), pp. 3–24.

    Google Scholar 

  8. D. A. Low and J. F. Dempsey, “Evaluation of the gamma dose distribution comparison method,” Med. Phys. 30, 2455–2464 (2003).

    Article  Google Scholar 

  9. J. E. Cygler, C. Lochrin, G. M. Daskalov, M. Howard, R. Zohr, and B. Esche, “Clinical use of a commercial Monte Carlo treatment planning system for electron beams,” Phys. Med. Biol. 50, 1029–1034 (2005).

    Article  Google Scholar 

  10. MDS Nordion, THERAPLAN plus Technical Reference Manual, 18th ed. (MDS Nordion, Canada, 2000).

  11. J. Palta, S. Kim, J. Li, and C. Liu, “Tolerance limits and action levels for planning and delivery of IMRT,” in Intensity-Modulated Radiation Therapy: The State of the Art, Ed. by J. R. Palta and T. R. Mackie, AAPM Medical Physics Monograph No. 29 (Medical Physics Publ., Madison, WI, 2003).

    Google Scholar 

  12. M. Yu, R. Sloboda, and B. Murray, “Linear accelerator photon beam quality at off-axis points,” Med. Phys. 24, 233–239 (1997).

    Article  Google Scholar 

  13. “Commissioning and quality assurance of computerized planning systems for radiation treatment of cancer,” Technical Reports Series No. 430 (Int. Atomic Energy Agency, Vienna, 2004).

  14. "Specification and acceptance testing of radiotherapy treatment planning systems," IAEA-TECDOC-1540 (Int. Atomic Energy Agency, Vienna, 2007).

  15. F. Verhaegen and J. Seuntjens, “Monte Carlo modelling of external radiotherapy photon beams,” Phys. Med. Biol. 48, 3401–3458 (2003).

    Article  Google Scholar 

  16. O. Chibani, B. Moftah, and C. M. Ma, “On Monte Carlo modeling of megavoltage photon beams: a revisited study on the sensitivity of beam parameters,” Med. Phys. 38, 188–201 (2011).

    Article  Google Scholar 

  17. J. V. Siebers, P. J. Keall, B. Libby, and R. Mohan, “Comparison of EGS4 and MCNP4b Monte Carlo codes for generation of photon phase space distributions for a varian 2100C,” Phys. Med. Biol. 44, 3009–3026 (1999).

    Article  Google Scholar 

  18. L. Apipunyasopon, S. Srisatit, and N. Phaisangittisakul, “An investigation of the depth dose in the buildup region, and surface dose for a 6-MeV therapeutic photon beam: Monte Carlo simulation and measurements,” J. Rad. Res., 374–382 (2013).

    Google Scholar 

  19. D. A. Low, W. B. Harms, S. Mutic, and J. A. Purdy, “A technique for the quantitative evaluation of dose distributions,” Med. Phys. 25, 656–661 (1998).

    Article  Google Scholar 

  20. K. F. Michael, J. K. Paul, and V. S. Jeffrey, “Photonbeam subsource sensitivity to the initial electron-beam parameters,” Med. Phys. 32, 1164–1175 (2005).

    Article  Google Scholar 

  21. K. Aljarrah, G. C. Sharp, T. Neicu, and S. B. Jiang, “Determination of the initial beam parameters in Monte Carlo linac simulation,” Med. Phys. 33, 850–858 (2006).

    Article  Google Scholar 

  22. A. Mesbahi, “Development a simple point source model for elekta SL-25 linear accelerator using MCNP4C Monte Carlo code,” Iran. J. Radiat. Res. 4, 7–14 (2006).

    Google Scholar 

  23. O. N. Vassiliev, U. Titt, S. F. Kry, F. Ponisch, M. Gillin, and R. Mohan, “Monte Carlo study of photon fields from a flattening filter-free clinical accelerator,” Med. Phys. 33, 820–827 (2006).

    Article  Google Scholar 

  24. O. N. Vassiliev, U. Titt, S. F. Kry, F. Poenisch, M. Gillin, and R. Mohan, “Dosimetric properties of photon beams from a flattening filter free clinical accelerator,” Phys. Med. Biol. 51, 1907–1917 (2006).

    Article  Google Scholar 

  25. D. Pearson, E. Parsai, and J. Fledmeier, “Evaluation of dosimetric properties of 6 and 10 MeV photon beams from a linear accelerator with no flattening filter,” Med. Phys. 33, 2099 (2006).

    Article  Google Scholar 

  26. D. Sheikh-Bagheri and D. W. Rogers, “Sensitivity of megavoltage photon beam Monte Carlo simulations to electron beam and other parameters,” Med. Phys. 29, 379–390 (2002).

    Article  Google Scholar 

  27. M. Oprea, C. Constantin, D. Mihailescu, and C. Borcia, “A Monte Carlo investigation of the influence of initial electron beam characteristics on the absorbed dose distributions obtained with a 9 MeV IORT accelerator,” UPB Sci. Bull., Ser. A 74, 153–166 (2012).

    Google Scholar 

  28. F. Verhaegen and J. Seuntjens, “Monte Carlo modelling of external radiotherapy photon beams,” Phys. Med. Biol. 48, 3401–3458 (2003).

    Article  Google Scholar 

  29. O. Chibani, B. Moftah, and C. M. Ma, “On Monte Carlo modeling of megavoltage photon beams: a revisited study on the sensitivity of beam parameters,” Med. Phys. 38, 188–201 (2011).

    Article  Google Scholar 

  30. J. V. Siebers, P. J. Keall, B. Libby, and R. Mohan, “Comparison of EGS4 and MCNP4b Monte Carlo codes for generation of photon phase space distributions for a varian 2100C,” Phys. Med. Biol. 44, 3009–3026 (1999).

    Article  Google Scholar 

  31. G. Doswell and J. Cunningham, “Modeling off-axis beam-softening to improve 3D dose-calculation accuracy for wedged photon beams,” in Proceedings of the 22nd Annual EMBS International Conference, 2000.

  32. B. Kadman, N. Chawapun, S. Ua-apisitwong, T. Asakit, N. Chumpu, J. Rueansri, “Consistency check of photon beam physical data after recommissioning process,” J. Phys.: Conf. Ser. 694, 012023 (2016).

    Google Scholar 

  33. M. Aljamal and A. Zakaria, “Monte Carlo modeling of a Siemens primus 6 MeV photon beam linear accelerator,” Austral. J. Basic Appl. Sci. 7, 340–346 (2013).

    Google Scholar 

  34. M. Muhammad, M. Wazir, S. Muhammad, A. Misbah, and M. Matiullah, “Accuracy checks of physical beam modifier factors algorithm used in computerized treatment planning system for a 15 MeV photon beam,” Rep. Pract. Oncol. Radiother. 14, 214–220 (2010).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Bencheikh.

Additional information

The article is published in the original.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bencheikh, M., Maghnouj, A. & Tajmouati, J. Photon beam softening coefficient determination with slab thickness in small filed size: Monte Carlo study. Phys. Part. Nuclei Lett. 14, 963–970 (2017). https://doi.org/10.1134/S1547477117060085

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1547477117060085

Keywords

Navigation