医用直线加速器入射电子束能量的快速模拟确定

doi:10.11889/j.0253-3219.2020.hjs.43.010202

Nuclear Techniques ›› 2020, Vol. 43 ›› Issue (1): 10202-010202.doi: 10.11889/j.0253-3219.2020.hjs.43.010202

• ACCELERATOR, RAY TECHNOLOGY AND APPLICATIONS • Previous Articles Next Articles

Fast confirmation of the incident electron energy by simulation for medical linac

Junjie HAN¹,Yongdong ZHUANG²,Xiaowei LIU²()

1. Department of Radiation Oncology, Guangdong General Hospital, Guangzhou 510080, China
2. School of Physics, Sun Yat-sen University, Guangzhou 510275, China

Received:2019-10-08 Revised:2019-11-27 Online:2020-01-15 Published:2020-01-21
Contact: Xiaowei LIU E-mail:stslxw@mail.sysu.edu.cn
About author:HAN Junjie, male, born in 1991, graduated from Sun Yat-sen University in 2016, focusing on radiotherapy
Supported by:
Joint Fund of National Natural Science Foundation of China(U1832128)

Abstract

Abstract: Background

Monte Carlo method provides a powerful tool for the simulation of particle transport in radiotherapy, hence can be used for determination of the target energy of the incident electron in medical linac simulation.

Purpose

This study aims to find a way that can confirm the incident electron energy accurately and quickly, and save time for Monte Carlo linac simulation.

Methods

First of all, Monte Carlo program package EGSnrc/BEAMnrc was employed to build up the beam models of Varian 600C, Trilogy and Edge FFF (Flattening Filter Free) model with the same nominal energy of simulation of 6 MV. Then, the dose distributions in different fields (3 cm×3 cm, 10 cm×10 cm, 40 cm×40 cm) using different incident electron energies in water phantom were calculated. Finally simulation results were compared and analyzed to find out a way to ensure the incident electron energy accurately and quickly.

Results

When the incident electron energy varies from 5.5 MeV to 6.5 MeV, percentage depth dose (PDD) is insensitive to the incident electron energy; the off axis ratio (OAR) profiles of 3 cm×3 cm and 10 cm×10 cm are insensitive to the incident electron energies, the OAR profiles of different incident electron energies are almost the same. The OAR profile of 40 cm×40 cm is very sensitive to the incident electron energy. Detailed results at 5 cm underwater show that when the incident electron energy increases by every 0.1 MeV, the average of OAR between the off axis distance from 14.5 cm to 19 cm decreases: 0.82% for Varian 600C 6 MV, 0.98% for Trilogy 6 MV, 0.47% for Edge 6 MV FFF, respectively. Combining the measured OAR profiles of large field (40 cm×40 cm) with the linear relationship between average OAR and the incident electron energy, the incident electron energy can be determined quickly. Compared with the measured values, the PDD and OAR using the fitting energy as input in water phantom, profiles differences are both within 1%.

Conclusions

This proposed method provides a way to ensure the incident electron energy accurately and quickly for medical linac.

Key words: Medical linear accelerator, Incident electron shooting energy, Varian 600C trilogy edge, Monte Carlo simulation

CLC Number:

TL53

Junjie HAN, Yongdong ZHUANG, Xiaowei LIU. Fast confirmation of the incident electron energy by simulation for medical linac[J].Nuclear Techniques, 2020, 43(1): 10202-010202.

Figures/Tables 8

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Table 1

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Table 3

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References 17

1	Verhaegen F, Seuntjens J. Monte Carlo modelling of external radiotherapy photon beams[J]. Physics in Medicine & Biology, 2003, 48(21): 107‒164. DOI: 10.1088/0031-9155/48/21/r01. doi: 10.1088/0031-9155/48/21/r01
2	Daryoush S B, Rogers D W O. Monte Carlo calculation of nine megavoltage photon beam spectra using the BEAM code[J]. Medical Physics, 2002, 29: 391‒402. DOI: 10.1118/1.1445413. doi: 10.1118/1.1445413
3	Ding G X. Energy spectra, angular spread, fluence profiles and dose distributions of 6 and 18 MV photon beams: results of Monte Carlo simulations for a varian 2100EX accelerator[J]. Physics in Medicine and Biology, 2002, 47(7): 1025‒1046. DOI: 10.1016/0021-9673(95)00582-8. doi: 10.1016/0021-9673(95)00582-8
4	Dalaryd M, Kragl G, Ceberg C, et al. A Monte Carlo study of a flattening filter-free linear accelerator verified with measurements[J]. Physics in Medicine and Biology, 2010, 55: 7333‒7344. DOI: 10.1088/0031-9155/55/23/010. doi: 10.1088/0031-9155/55/23/010
5	Paynter D, Weston S J, Cosgrove V P. Beam characteristics of energy-matched flattening filter free beams[J]. Medical Physics, 2014, 41: 052103. DOI: 10.1118/1.4871615. doi: 10.1118/1.4871615
6	Kragl G, Wetterstedt S, Knusl B, et al. Dosimetric characteristics of 6 and 10 MV unflattened photon beams [J]. Radiotherapy and Oncology, 2009, 93(1): 141‒146. DOI: 10.1016/j.radonc.2009.06.008. doi: 10.1016/j.radonc.2009.06.008
7	Almberg S S, Frengen J, Lindmo T. Monte Carlo study of in-field and out-of-field dose distributions from a linac operating with and without a flattening-filter[J]. Medical Physics, 2012, 39(8): 5194‒5203. DOI: 10.1118/1.4738963. doi: 10.1118/1.4738963
8	Almberg S S, Frengen J, Kylling A, et al. Monte Carlo linear accelerator simulation of megavoltage photon beams: independent determination of initial beam parameters[J]. Medical Physics, 2012, 39(1): 40‒46. DOI:10.1118/1.3668315. doi: 10.1118/1.3668315
9	Pena J, González-Castaño D M, Gómez F, et al. Automatic determination of primary electron beam parameters in Monte Carlo simulation[J]. Medical Physics, 2007, 34(3): 1076‒1084. DOI: 10.1118/1.2514155. doi: 10.1118/1.2514155
10	Aljarrah K, Sharp G C, Neicu T, et al. Determination of the initial beam parameters in Monte Carlo linac simulation[J]. Medical Physics, 2006, 33(4): 850‒858. DOI: 10.1118/1.2168433. doi: 10.1118/1.2168433
11	Daryoush S B, Rogers D W O. Sensitivity of megavoltage photon beam Monte Carlo simulations to electron beam and other parameters[J]. Medical Physics, 2002, 29(3): 391‒402. DOI: 10.1118/1.1446109. doi: 10.1118/1.1446109
12	Keall P J, Siebers J V, Libby B, et al. Determining the incident electron fluence for Monte Carlo-based photon treatment planning using a standard measured data set[J]. Medical Physics, 2003, 30(4): 574‒582. DOI: 10.1118/1.1561623. doi: 10.1118/1.1561623
13	Omar C, Belal M, Charlie C M M. On Monte Carlo modeling of megavoltage photon beams: a revisited study on the sensitivity of beam parameters[J]. Medical Physics, 2011, 38(1): 188‒201. DOI: 10.1118/1.3523625. doi: 10.1118/1.3523625
14	Kawrakow I, Rogers D. The EGSnrc code system: Monte Carlo simulation of electron and photon transport[R].>Ottawa: National Research Council of Canada, 2000.
15	Rogers D W O, Walters B, Kawrakow I. BEAMnrc user's manual[R]. NRC Report PIRS,2001, 509.
16	Walters B, Kawrakow I, Rogers D W O. DOSXYZnrc users manual[R]. NRCC Report PIRS -794 rev B, 2004.
17	Tzedakis A, John E, Mazonakis M, et al. Influence of initial electron beam parameters on Monte Carlo calculated absorbed dose distributions for radiotherapy photon beams[J]. Medical Physics, 2004, 31(4): 907‒913. DOI: 10.1118/1.1668551. doi: 10.1118/1.1668551

	Varian 600C 6 MV / %	Trilogy 6 MV / %	Edge 6 MV FFF / %
3 cm×3 cm	1.5	2.4	2.2
10 cm×10 cm	1.6	1.9	1.8
40 cm×40 cm	1.1	1.4	1.4

	拟合公式 Fitting formula	线性相关性 Linear correlation
Varian 600C 6 MV	R=1.500 43-0.082 57E	0.997
Varian Trilogy 6 MV	R=1.585 50-0.097 60E	0.991
Varian Edge 6 MV FFF	R=0.887 21-0.047 29E	0.997

	测量的OAR平均值 Average of OAR	反推的打靶能量 Energy of calculated / MeV
Varian 600C	1.044 6	5.52
Varian Trilogy 6 MV	1.031 9	5.67
Varian Edge 6 MV FFF	0.615 2	5.75

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Fast confirmation of the incident electron energy by simulation for medical linac

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