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A comparison of Monte Carlo, anisotropic analytical algorithm (AAA) and Acuros XB algorithms in assessing dosimetric perturbations during enhanced dynamic wedged radiotherapy deliveries in heterogeneous media

Published online by Cambridge University Press:  06 August 2018

Ashfaq Zaman ,
Muhammad Basim Kakakhel Open the ORCID record for Muhammad Basim Kakakhel [Opens in a new window]  and
Amjad Hussain
Ashfaq Zaman
Affiliation:
Department of Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan Swat Institute of Nuclear Medicine Oncology and Radiotherapy (SINOR), Saidu Sharif, Swat, Pakistan
Muhammad Basim Kakakhel*
Affiliation:
Department of Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
Amjad Hussain
Affiliation:
Agha Khan University Hospital, Karachi, Pakistan Western Manitoba Cancer Centre Brandon, Manitoba, Canada
*
Author for correspondence: Muhammad Basim Kakakhel, Department of Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan. Tel: +92 51 9248611. Fax: +92 51 9248600. E-mail: basim@pieas.edu.pk
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Abstract

Background

A comparison of anisotropic analytical algorithm (AAA) and Acuros XB (AXB) dose calculation algorithms with Electron Gamma Shower (EGSnrc) Monte Carlo (MC) for modelling lung and bone heterogeneities encountered during enhanced dynamic wedged (EDWs) radiotherapy dose deliveries was carried out.

Materials and methods

In three heterogenous slab phantoms: water–bone, lung–bone and bone–lung, wedged percentage depth doses with EGSnrc, AAA and AXB algorithms for 6 MV photons for various field sizes (5×5, 10×10 and 20×20 cm2) and EDW angles (15°, 30°, 45° and 60°) have been scored.

Results

For all the scenarios, AAA and AXB results were within ±1% of the MC in the pre-inhomogeneity region. For water–bone AAA and AXB deviated by 6 and 1%, respectively. For lung–bone an underestimation in lung (AAA: 5%, AXB: 2%) and overestimation in bone was observed (AAA: 13%, AXB: 4%). For bone–lung phantom overestimation in bone (AAA: 7%, AXB: 1%), a lung underdosage (AAA: 8%, AXB: 5%) was found. Post bone up to 12% difference in the AAA and MC results was observed as opposed to 6% in case of AXB.

Conclusion

This study demonstrated the limitation of the AAA (in certain scenarios) and accuracy of AXB for dose estimation inside and around lung and bone inhomogeneities. The dose perturbation effects were found to be slightly dependent on the field size with no obvious EDW dependence.

Keywords

AAAAcuros XBEDWsEGSnrcheterogeneities
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 18 , Issue 1 , March 2019 , pp. 75 - 81
Copyright
© Cambridge University Press 2018 

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Footnotes

Cite this article: Zaman A, Kakakhel MB, Hussain A. (2019) A comparison of Monte Carlo, anisotropic analytical algorithm (AAA) and Acuros XB algorithms in assessing dosimetric perturbations during enhanced dynamic wedged radiotherapy deliveries in heterogeneous media. Journal of Radiotherapy in Practice18: 75–81. doi: 10.1017/S1460396918000262

References

1. Spezi, E, Lewis, G. An overview of Monte Carlo treatment planning for radiotherapy. Radiat Prot Dosim 2008; 131 (1): 123–129.Google Scholar
2. Beavis, A, Weston, S, Whitton, V. Implementation of the Varian EDW into a commercial RTP system. Phys Med Biol 1996; 41 (9): 1691–1704.Google Scholar
3. Bush, K, Gagne, I, Zavgorodni, S, Ansbacher, W, Beckham, W. Dosimetric validation of Acuros® XB with Monte Carlo methods for photon dose calculations. Med Phys 2011; 38 (4): 2208–2221.Google Scholar
4. Papanikolaou, N, Stathakis, S. Dose-calculation algorithms in the context of inhomogeneity corrections for high energy photon beams. Med Phys 2009; 36 (10): 4765–4775.Google Scholar
5. Bragg, C M, Wingate, K, Conway, J. Clinical implications of the anisotropic analytical algorithm for IMRT treatment planning and verification. Radiother Oncol 2008; 86 (2): 276–284.Google Scholar
6. Ono, K, Endo, S, Tanaka, K, Hoshi, M, Hirokawa, Y. Dosimetric verification of the anisotropic analytical algorithm in lung equivalent heterogeneities with and without bone equivalent heterogeneities. Med Phys 2010; 37 (8): 4456–4463.Google Scholar
7. Dunn, L, Lehmann, J, Lye, J et al National dosimetric audit network finds discrepancies in AAA lung inhomogeneity corrections. Phys Med 2015; 31: 435–441.Google Scholar
8. Failla, G A, Wareing, T, Archambault, Y, Thompson, S. Acuros XB Advanced Dose Calculation for the Eclipse Treatment Planning System. Palo Alto, CA: Varian Medical Systems, USA, 2010.Google Scholar
9. Ma, C-M, Jiang, S B. Monte Carlo modelling of electron beams from medical accelerators. Phys Med Biol 1999; 44 (12): R157–R189.Google Scholar
10. Rogers, D. Fifty years of Monte Carlo simulations for medical physics. Phys Med Biol 2006; 51 (13): R287.Google Scholar
11. Kawrakow, I, Rogers, D. The EGSnrc code system: Monte Carlo simulation of electron and photon transport. NRCC Report Pirs-701 (2001).Google Scholar
12. Carrasco, P, Jornet, N, Duch, MA et al. Comparison of dose calculation algorithms in phantoms with lung equivalent heterogeneities under conditions of lateral electronic disequilibrium. Med Phys 2004; 31 (10): 2899–2911.Google Scholar
13. Carrasco, P, Jornet, N, Duch, M A et al. Comparison of dose calculation algorithms in slab phantoms with cortical bone equivalent heterogeneities. Med Phys 2007; 34 (8): 3323–3333.Google Scholar
14. Bragg, CM, Conway, J. Dosimetric verification of the anisotropic analytical algorithm for radiotherapy treatment planning. Radiother Oncol 2006; 81 (3): 315–323.Google Scholar
15. Sterpin, E, Tomsej, M, De Smedt, B, Reynaert, N, Vynckier, S. Monte Carlo evaluation of the AAA treatment planning algorithm in a heterogeneous multilayer phantom and IMRT clinical treatments for an Elekta SL25 linear accelerator. Med Phys 2007; 34 (5): 1665–1677.Google Scholar
16. Khan, F M. The Physics of Radiation Therapy. Philadelphia, PA: Lippincott Williams & Wilkins, 2010.Google Scholar
17. Aslam, A, Kakakhel, M B, Shahid, S A, Younas, L, Zareen, S. Soft tissue and water substitutes for megavoltage photon beams: an EGSnrc-based evaluation. J Appl Clin Med Phys 2016; 17 (1): 408–415.Google Scholar
18. Rogers, D, Walters, B, Kawrakow, I. BEAMnrc users manual. Ottawa, Canada: NRC Report PIRS, 2001.Google Scholar
19. Kakakhel, M, Baveas, E, Fielding, A L, Kairn, T, Kenny, J, Trapp, J. Validation and automation of the DYNJAWS component module of the BEAMnrc Monte Carlo code. Australas Phys Eng Sci Med 2011; 34 (1): 83–90.Google Scholar