Benchmarking of Monte Carlo model of Siemens Oncor® linear accelerator for 18MV photon beam: Determination of initial electron beam parameters

Najafzadeh, Milad; Hoseini-Ghafarokhi, Mojtaba; Bolagh, Rezgar Shahi Mayn; Haghparast, Mohammad; Zarifi, Shiva; Nickfarjam, Abolfazl; Farhood, Bagher; Chow, J

doi:10.3233/xst-190568

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…Analytical dose calculation algorithms suffer from a clinically significant disagreement with measurement when treatment planning involves inhomogeneous areas. [ 1 ] These errors can become a dominant source of inaccuracy in treatment planning, especially in precision dose delivery techniques. Considering the uncertainties of dosimetry and treatment plan delivery, the importance of accurate dose calculations is highlighted.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity analysis of a 6 MeV Photon Beam Monte Carlo model

Atarod,

Shami,

Shokrani

et al. 2023

J Med Signals Sens

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Section: Introductionmentioning

confidence: 99%

“…However, the benchmarking process can become more targeted using sensitivity analysis of the calculated absorbed dose distributions to different parameters of linac and electron beam properties. [ 1 4 5 6 7 8 9 ]…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of a 6 MeV Photon Beam Monte Carlo model

Atarod,

Shami,

Shokrani

et al. 2023

J Med Signals Sens

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“…ese steps are ultimately designed to obtain the signi cant parameters in TPS model building. Many investigations have been conducted to benchmark the incident electrons of other medical linear accelerators such as Elekta [3][4][5], Varian [6][7][8][9], and Siemens [10,11], but no dosimetric studies on the XHA600D accelerator have yet been found. And unlike these references, this study speci cally analyzed the e ects of these four incident electron parameters on PDDs and OARs in di erent beam elds and the details of the benchmarking process to obtain accurate incident electron information of the XHA600D accelerator.…”

Section: Introductionmentioning

confidence: 99%

The Study of Dosimetric Characteristics of the XHA600D Medical Linear Accelerator Based on a Monte Carlo Code

Wang

Cui

et al. 2022

Science and Technology of Nuclear Installations

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By investigating the influence of initial electrons on dosimetric characteristics, reasonable incident electron parameters for the nominal 6 MV photon beam of the XHA600D accelerator are finally established, i.e., a 6 MeV monoenergetic electron beam with a radial intensity FWHM of 2.5 mm and an angular divergency of 0.15°. Based on reasonable initial parameters, Percentage Depth Doses (PDDs), Off-Axis Ratios (OARs), total scatter factors, beam qualities, and penumbra widths of both flatteningfilter (FF) and flattening-filter-free (FFF) beams for fields ranging from 4 × 4 to 30 × 30 cm2 are simulated systematically with EGSnrc codes. Not only the simulated dosimetric properties are in excellent agreement with the measurements, but also the dosimetric discrepancies between FF and FFF beams are consistent with the laws of previous studies on other accelerators. Therefore, reasonable incident electron parameters are able to accurately verify the performance of the XHA600D accelerator and can be used for further dosimetry research.

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“…proposing various experimental setups and methods of such analysis [6][7][8][9][11][12][13][14][15][16][17][18]. In the study of Almberg et al [9] an algorithm is proposed to determine values of two parameters (nominal energy and spot size) of the primary electron beam model, basing on depth and lateral photon beam profiles measured for three fields: 2x2, 10x10, and 20x20 cm2.…”

Section: Introductionmentioning

confidence: 99%

“…The last two steps require an extensive MC simulation effort while being based on a trial-and-error approach. Other studies of [13][14][15][16][17][18] are also based on a trial-and-error approach which, while being computationally demanding, offers no systematic procedure for estimating parameter values in the applied model of the primary electron beam.…”

Section: Introductionmentioning

confidence: 99%

DeepBeam – A Machine Learning Framework For Tuning The Primary Electron Beam of The PRIMO Monte Carlo Software

Tabor

Kabat

Waligórski

2021

Preprint

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BackgroundAny Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any single accelerator are unique and generally unknown, an appropriate model of an electron beam must be assumed before MC simulations can be run. The purpose of the present study is to develop a flexible framework with suitable regression models for estimating parameters of the model of primary electron beam in simulators of medical linear accelerators, basing on real reference dose profiles measured in a water phantom. MethodsAll simulations were run using PRIMO MC simulator. Two regression models for estimating the parameters of the simulated primary electron beam, both based on machine learning, were developed. The first model applies Principal Component Analysis to measured dose profiles in order to extract principal features of the shapes of the these profiles. The PCA-obtained features are then used by Support Vector Regressors to estimate the parameters of the model of the electron beam. The second model, based on deep learning, consists of a set of encoders processing measured dose profiles, followed by a sequence of fully connected layers acting together, which solve the regression problem of estimating values of the electron beam parameters directly from the measured dose profiles. Results of the regression are then used to reconstruct the dose profiles, basing on the PCA model. Agreement between the measured and reconstructed profiles can be further improved by an optimization procedure resulting in the final estimates of the parameters of the model of the primary electron beam. These final estimates are then used to determine dose profiles in MC simulations.ResultsAnalysed were a set of actually measured (real) dose profiles of 6 MV beams from a real Varian 2300 C/D accelerator, a set of simulated training profiles, and a separate set of simulated testing profiles, both generated for a range of parameters of the primary electron beam of the Varian 2300 C/D PRIMO simulator. Application of the two-stage procedure based on regression followed by reconstruction-based minimization of the difference between measured (real) and reconstructed profiles resulted in achieving consistent estimates of electron beam parameters and in a very good agreement between the measured and simulated photon beam profiles.ConclusionsThe proposed framework is a readily applicable and customizable tool which may be applied in tuning virtual primary electron beams of Monte Carlo simulators of linear accelerators. The codes, training and test data, together with some trained models and readout procedures, are freely available at the site: https://github.com/taborzbislaw/DeepBeam.

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Benchmarking of Monte Carlo model of Siemens Oncor® linear accelerator for 18MV photon beam: Determination of initial electron beam parameters

Cited by 4 publications

References 39 publications

Sensitivity analysis of a 6 MeV Photon Beam Monte Carlo model

Sensitivity analysis of a 6 MeV Photon Beam Monte Carlo model

The Study of Dosimetric Characteristics of the XHA600D Medical Linear Accelerator Based on a Monte Carlo Code

DeepBeam – A Machine Learning Framework For Tuning The Primary Electron Beam of The PRIMO Monte Carlo Software

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