This study aims to benchmark a Monte Carlo (MC) model of the 18 MV photon beam produced by the Siemens Oncor® linac using the BEAMnrc and DOSXYZnrc codes.
METHODS:
By matching the percentage depth doses and beam profiles calculated by MC simulations with measurements, the initial electron beam parameters including electron energy, full width at half maximum (spatial FWHM), and mean angular spread were derived for the 10×10 cm2 and 20×20 cm2 field sizes. The MC model of the 18 MV photon beam was then validated against the measurements for different field sizes (5×5, 30×30 and 40×40 cm2) by gamma index analysis.
RESULTS:
The optimum values for electron energy, spatial FWHM and mean angular spread were 14.2 MeV, 0.08 cm and 0.8 degree, respectively. The MC simulations yielded the comparable measurement results of these optimum parameters. The gamma passing rates (with acceptance criteria of 1% /1 mm) for percentage depth doses were found to be 100% for all field sizes. For cross-line profiles, the gamma passing rates were 100%, 97%, 95%, 96% and 95% for 5×5, 10×10, 20×20, 30×30 and 40×40 cm2 field sizes, respectively.
CONCLUSIONS:
By validation of the MC model of Siemens Oncor® linac using various field sizes, it was found that both dose profiles of small and large field sizes were very sensitive to the changes in spatial FWHM and mean angular spread of the primary electron beam from the bending magnet. Hence, it is recommended that both small and large field sizes of the 18 MV photon beams should be considered in the Monte Carlo linac modeling.
ToossiM.T.B., et al., Assessment of accuracy of out-of-field dose calculations by TiGRT treatment planning system in radiotherapy, Journal of Cancer Research and Therapeutics14(3) (2018), 634.
2.
DelaneyG., et al., The role of radiotherapy in cancer treatment: Estimating optimal utilization from a review of evidence-based clinical guidelines, Cancer104(6) (2005), 1129–1137.
3.
BeggA.C., StewartF.A. and VensC., Strategies to improve radiotherapy with targeted drugs, Nature Reviews Cancer11(4) (2011), 239.
4.
FarhoodB., GerailyG. and AbtahiS.M.M., A systematic review of clinical applications of polymer gel dosimeters in radiotherapy, Applied Radiation and Isotopes143 (2019), 47–59.
5.
MannP., et al., 3D dosimetric validation of motion compensation concepts in radiotherapy using an anthropomorphic dynamic lung phantom, Physics in Medicine & Biology62(2) (2016), 573.
6.
AsadiA., et al., Monte Carlo evaluation of the potential benefits of flattening filter free beams from the Oncor® clinical linear accelerator, Journal of X-ray Science and Technology26(2) (2018), 281–302.
7.
ToossiM.T.B., FarhoodB. and SoleymanifardS., Evaluation of dose calculations accuracy of a commercial treatment planning system for the head and neck region in radiotherapy, Reports of Practical Oncology & Radiotherapy22(5) (2017), 420–427.
8.
NajafzadehM., et al., Dosimetric verification of lung phantom calculated by collapsed cone convolution: A Monte Carlo and experimental evaluation, Journal of X-ray Science and Technology27 (2019), 161–175.
9.
PapanikolaouN., et al., Tissue inhomogeneity corrections for megavoltage photon beams, AAPM Task Group65 (2004), 1–142.
10.
KhanF.M., GibbonsJ.P., Khan’s the physics of radiation therapy. 2014: Lippincott Williams & Wilkins.
11.
ParwaieW., et al., Different dosimeters/detectors used in small-field dosimetry: Pros and cons, Journal of Medical Signals and Sensors8(3) (2018), 195.
12.
SecoJ., VerhaegenF., Monte Carlo techniques in radiation therapy. 2013: CRC press.
13.
ChettyI.J., et al., Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning, Medical Physics34(12) (2007), 4818–4853.
14.
ReynaertN., et al., Monte Carlo treatment planning for photon and electron beams, Radiation Physics and Chemistry76(4) (2007), 643–686.
15.
AlmbergS.S., et al., Monte Carlo linear accelerator simulation of megavoltage photon beams: Independent determination of initial beam parameters, Medical Physics39(1) (2012), 40–47.
16.
FaddegonB.A., et al., Treatment head disassembly to improve the accuracy of large electron field simulation, Medical Physics36(10) (2009), 4577–4591.
17.
SchreiberE. and FaddegonB., Sensitivity of large-field electron beams to variations in a Monte Carlo accelerator model, Physics in Medicine & Biology50(5) (2005), 769.
18.
MaC.-M., et al., Accurate characterization of Monte Carlo calculated electron beams for radiotherapy, Medical Physics24(3) (1997), 401–416.
19.
Sheikh-BagheriD. and RogersD., Monte Carlo calculation of nine megavoltage photon beam spectra using the BEAM code, Medical Physics29(3) (2002), 391–402.
20.
DingG.X., Energy spectra, angular spread, fluence profiles and dose distributions of 6 and 18MV photon beams: Results of Monte Carlo simulations for a Varian 2100EX accelerator, Physics in Medicine & Biology47(7) (2002), 1025.
21.
ChibaniO., MoftahB. and MaC.M.C., On Monte Carlo modeling of megavoltage photon beams: A revisited study on the sensitivity of beam parameters, Medical Physics38(1) (2011), 188–201.
22.
KeallP.J., et al., Determining the incident electron fluence for Monte Carlo-based photon treatment planning using a standard measured data set, Medical Physics30(4) (2003), 574–582.
23.
ChibaniO. and MaC.M.C., On the discrepancies between Monte Carlo dose calculations and measurements for the Varian photon beam, Medical Physics34(4) (2007), 1206–1216.
24.
Sheikh-BagheriD. and RogersD., Sensitivity of megavoltage photon beam Monte Carlo simulations to electron beam and other parameters, Medical Physics29(3) (2002), 379–390.
25.
TzedakisA., et al., Influence of initial electron beam parameters on Monte Carlo calculated absorbed dose distributions for radiotherapy photon beams, Medical Physics31(4) (2004), 907–913.
26.
DavisA.M., et al., Collision-avoiding imaging trajectories for linac mounted cone-beam CT, Journal of X-ray Science and Technology27(1) (2019), 1–16.
27.
SawkeyD. and FaddegonB., Determination of electron energy, spectral width, and beam divergence at the exit window for clinical megavoltage x-ray beams, Medical Physics36(3) (2009), 698–707.
28.
JabbariK., et al., Monte carlo simulation of siemens oncor linear accelerator with beamnrc and dosxyZnrc code, Journal of Medical Signals and Sensors3(3) (2013), 172.
29.
DingG.X., Dose discrepancies between Monte Carlo calculations and measurements in the buildup region for a high-energy photon beam, Medical Physics29(11) (2002), 2459–2463.
30.
Hartmann SiantarC., et al., Description and dosimetric verification of the PEREGRINE Monte Carlo dose calculation system for photon beams incident on a water phantom, Medical Physics28(7) (2001), 1322–1337.
31.
MohammedM., et al., Monte Carlo simulation of Varian Linac for 6MV photon beam with BEAMnrc code, Radiation Physics and Chemistry144 (2018), 69–75.
WaltersR., KawrakowI., RogersW., DOSXYZnrc users manual National Research Council of Canada Report PIRS-794 rev. 2009, B.
34.
T. IAEA, 398. Absorbed dose determination in external beam radiotherapy: An International Code of Practice for Dosimetry based on standards of absorbed dose to water. Vienna International Atomic Energy Agency, 2000.
35.
VerhaegenF. and SeuntjensJ., Monte Carlo modelling of external radiotherapy photon beams, Physics in Medicine & Biology48(21) (2003), R107.
36.
BakkaliJ.E. and BardouniT.E., Validation of Monte Carlo Geant4 code for a 6MV Varian linac, Journal of King Saud University-Science29(1) (2017), 106–113.
37.
M, G., 1D, 2D, or 3D gamma computation in MATLAB. 2018.
38.
LowD.A., et al., A technique for the quantitative evaluation of dose distributions, Medical Physics25(5) (1998), 656–661.
39.
FixM.K., KeallP.J. and SiebersJ.V., Photon-beam subsource sensitivity to the initial electron-beam parameters, Medical Physics32(4) (2005), 1164–1175.
40.
SohR.C.X., et al., A Gamma Analysis Approach to Determine Optimal Parameters for Monte Carlo Simulation of 6MV Varian Clinac iX Photon Beam, Journal of Health Science5 (2017), 36–43.
41.
AljarrahK., et al., Determination of the initial beam parameters in Monte Carlo linac simulation, Medical Physics33(4) (2006), 850–858.
42.
MishraS., et al., Monte carlo investigation of photon beam characteristics and its variation with incident electron beam parameters for indigenous medical linear accelerator, Journal of Medical Physics43(1) (2018), 1.
43.
PenaJ., et al., Automatic determination of primary electron beam parameters in Monte Carlo simulation, Medical Physics34(3) (2007), 1076–1084.
44.
MohammedM., et al., Validation of BEAMnrc Monte Carlo model for a 12MV photon beam, Journal of King Saud University-Science30(4) (2018), 537–543.
45.
ChangK.-P., WangZ.-W. and ShiauA.-C., Determining optimization of the initial parameters in Monte Carlo simulation for linear accelerator radiotherapy, Radiation Physics and Chemistry95 (2014), 161–165.
46.
GholampourkashiS., et al., Monte Carlo and analytic modeling of an Elekta Infinity linac with Agility MLC: Investigating the significance of accurate model parameters for small radiation fields, Journal of Applied Clinical Medical Physics20(1) (2019), 55–67.
47.
JiangS.B., BoyerA.L. and MaC.M.C., Modeling the extrafocal radiation and monitor chamber backscatter for photon beam dose calculation, Medical Physics28(1) (2001), 55–66.
