Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-24T00:27:31.193Z Has data issue: false hasContentIssue false
  • English
  • Français

The comparison between 6 MV Primus LINAC simulation output using EGSnrc and commissioning data

Published online by Cambridge University Press:  21 January 2018

Mohammad Davoudi ,
Ali Shabestani Monfared  and
Mohammad Rahgoshay
Mohammad Davoudi
Affiliation:
Department of Medical Radiation Engineering, Central Tehran Branch, Islamic Azad University, TehranIran
Ali Shabestani Monfared*
Affiliation:
Cancer Research Center, Medical Physics Department, Rajaee Oncology Hospital, Babol University of Medical Sciences, BabolIran
Mohammad Rahgoshay
Affiliation:
Department of Nuclear Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
*
†Correspondence to: Ali Shabestani Monfared Cell, Cancer Research Center, Medical Physics Department, Rajaee Oncology Hospital, Babol University of Medical Sciences, Babol, Iran Tel: 98 9111230475, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction

Monte Carlo calculation method is considered to be the most accurate method for dose calculation in radiotherapy. The purpose of this research is comparison between 6 MV Primus LINAC simulation output with commissioning data using EGSnrc and build a Monte Carlo geometry of 6 MV Primus LINAC as realistically as possible. The BEAMnrc and DOSXYZnrc (EGSnrc package) Monte Carlo model of the LINAC head was used as a benchmark.

Methods

In the first part, the BEAMnrc was used for the designing of the LINAC treatment head. In the second part, dose calculation and for the design of 3D dose file were produced by DOSXYZnrc. The simulated PDD and beam profile obtained were compared with that calculated using commissioning data. Good agreement was found between calculated PDD (1·1%) and beam profile using Monte Carlo simulation and commissioning data. After validation, TPR20,10, TMR and Sp values were calculated in five different field.

Results

Good agreement was found between calculated values by using Monte Carlo simulation and commissioning data. Average differences for five field sizes in this approach is about 0·83% for Sp. for TPR20,10 differences for field sizes 10×10 cm2 is 0·29% and for TMR in five field sizes, the average value is ~1·6%.

Conclusion

In conclusion, the BEAMnrc and DOSXYZnrc codes package have very good accuracy in calculating dose distribution for 6 MV photon beam and it can be considered as a promising method for patient dose calculations and also the Monte Carlo model of primus linear accelerator built in this study can be used as method to calculate the dose distribution for cancer patients.

Keywords

BEAMnrcLINACMonte Carlo simulationradiotherapyTPR
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 17 , Issue 3 , September 2018 , pp. 302 - 308
Copyright
© Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Toossi, M B, Momennezhad, M, Hashemi, M. Monte Carlo simulation of a linear accelerator and electron beam parameters used in radiotherapy. IJMP 2009; 6 (2): 11–18.Google Scholar
2. Serrano, B, Hachem, A, Franchisseu, E et al. Monte Carlo simulation of a medical linear accelerator for radiotherapy use. Radiat Prot Dosimetry 2006; 119 (1-4): 506–509.Google Scholar
3. Khan, F M, Gibbons, J. P. The Physics of Radiation Therapy, 4th edition. Philadelphia, PA: Lippincott Williams & Wilkins, 2014: 153–154.Google Scholar
4. Podgorsak, E B. Radiation Oncology Physics: A Handbook for Teachers and Students. IAEA, Vienna, Austria, 2005.Google Scholar
5. IAEA. Absorbed Dose Determination in External Beam Radiotherapy (TRS 398)12. IAEA, Vienna, Austria, 2000: 68–69.Google Scholar
6. Pena, J, Franco, L, Gomez, F et al. Commissioning of a medical accelerator photon beam Monte Carlo simulation using wide-field profiles. Phys Med Biol 2004; 49: 4929–4942.Google Scholar
7. Aljamal, M, Zakaria, A. Monte Carlo modeling of a Siemens Primus 6 MV photon beam linear accelerator. Aust J Basic Appl Sci 2013; 7 (10): 340–346.Google Scholar
8. Jabbari, K, Sberi, H, Tvakoli, M, Amouheydari, A. Monte Carlo simulation of Siemens ONCOR linear accelerator with BEAMnrc and DOSXYZnrc code. J Med Signals Sens 2013; 3 (3): 172–174.Google Scholar
9. Stathakis, S, Balbi, F, Chronopoulos, A, Papanikolaou, N. Monte Carlo modeling of linear accelerator using distributed computing. J BUON 2016; 21 (1): 252–260.Google Scholar
10. McKerracher, C, Thwaites, D. Phantom scatter factors for small MV photon fields. Radiother Oncol 2008; 86: 272–275.Google Scholar
11. Chang, K P, Wei Wang, Z, Shiau, A. Determining optimization of the initial parameters in Monte Carlo simulation for linear accelerator radiotherapy. Radiation Physics and Chemistry: Elsevier, 2014; 95: 161–165.Google Scholar
12. Toutaoui, A, Ait chikh, S, Khelassi-Toutaoui, N, Hattali, B. Monte Carlo photon beam modeling and commissioning for radiotherapy dose calculation algorithm. Physica Medica: Elsevier, 2014: 1–5.Google Scholar
13. Ding, G X. Using Monte Carlo simulations to commission photon beam output factors-a feasibility study. Phys Med Biol 2003; 48: 3865–3874.Google Scholar
14. Verhaegen, F., Seuntjens, J. Monte Carlo modelling of external radiotherapy photon beams. Phys Med Biol 2003; 48: R107–R164.Google Scholar