1393 poster DETERMINATION OF PRIMARY ELECTRON BEAM PARAMETERS IN MONTE CARLO SIMULATION OF MEDICAL LINAC

Abstract Background Any 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 using real reference dose profiles measured in a water phantom. Methods All 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 based 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. Results Analysed 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. Conclusions The 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 readout procedures, are freely available at the site: https://github.com/taborzbislaw/DeepBeam.

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Monte Carlo modelling of clinical accelerator beams and estimation of primary electron beam parameters

Radioprotection ◽

10.1051/radiopro/2018001 ◽

2018 ◽

Vol 53 (1) ◽

pp. 61-66

Author(s):

S. Horová ◽

L. Judas

Keyword(s):

Monte Carlo ◽

Electron Beam ◽

Confidence Region ◽

Primary Electron ◽

Head Model ◽

Primary Electron Beam ◽

Regression Methods ◽

Dosimetric Data ◽

The Mean ◽

Beam Parameters

The accuracy of Monte Carlo simulations of clinical photon beams in radiation oncology is dependent on the linac head model accuracy and on parameters of the primary electron beam. While the internal composition and geometry of the accelerator head are known precisely, at least in principle, the energy spectrum and the spatial characteristics of the primary electron beam are unknown and immeasurable. The mean energy and FWHM of the electron beam are commonly estimated by comparing the simulation results with measured dosimetric data. Percentage depth doses (PDDs) and dose profiles are sensitive to changes in the electron beam parameters and are therefore in general used for the comparison. In the published studies which deal with parameter estimation, the determination of electron beam parameters is typically performed through a trial and error process. As to the parameter optimization, there is no unified methodology agreed upon, and the uncertainty of the resulting parameter values is usually not quantified by the authors. The aim of our work was not only to estimate the mean energy and the FWHM of the primary electron beam, but also to determine the confidence region of the optimized values in a defined and repeatable way. A model of Varian Clinac 2100C/D linear accelerator 6 MV photon beam was built in the EGSnrc/BEAMnrc Monte Carlo system. PDDs and dose profiles for different field sizes and different depths were obtained from water phantom measurements. We show that an approach based on a large number of simulations, each with a relatively low number of primary particles, in combination with non-linear regression methods allows to find both the optimized values of the electron beam parameters and their common 95% confidence region.

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

10.21203/rs.3.rs-425712/v1 ◽

2021 ◽

Author(s):

Zbisław Tabor ◽

Damian Kabat ◽

Michael Waligórski

Keyword(s):

Machine Learning ◽

Monte Carlo ◽

Electron Beam ◽

Regression Models ◽

Primary Electron ◽

Support Vector ◽

Linear Accelerators ◽

Primary Electron Beam ◽

Measured Dose ◽

Beam Parameters

Abstract 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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Monte Carlo simulation of the secondary electron yield of an insulating target bombarded by a defocused primary electron beam

Journal of the European Ceramic Society ◽

10.1016/j.jeurceramsoc.2005.03.144 ◽

2005 ◽

Vol 25 (12) ◽

pp. 2805-2808 ◽

Cited By ~ 6

Author(s):

R. Renoud ◽

F. Mady ◽

J. Bigarré ◽

J.-P. Ganachaud

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Electron Beam ◽

Secondary Electron ◽

Primary Electron ◽

Primary Electron Beam ◽

Secondary Electron Yield ◽

Electron Yield

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

Journal of X-Ray Science and Technology ◽

10.3233/xst-190568 ◽

2020 ◽

Vol 27 (6) ◽

pp. 1047-1070

Author(s):

Milad Najafzadeh ◽

Mojtaba Hoseini-Ghafarokhi ◽

Rezgar Shahi Mayn Bolagh ◽

Mohammad Haghparast ◽

Shiva Zarifi ◽

...

Keyword(s):

Monte Carlo ◽

Electron Beam ◽

Linear Accelerator ◽

Monte Carlo Model ◽

Photon Beam ◽

Initial Electron ◽

Beam Parameters

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Electron beam scattering in an organic specimen

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100108179 ◽

1978 ◽

Vol 36 (1) ◽

pp. 206-207

Author(s):

Ilesanmi Adesida

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Electron Beam ◽

Energy Dissipation ◽

Primary Electron ◽

Organic Sample ◽

Direct Technique ◽

Beam Scattering ◽

Dissipation Processes ◽

Scanning Electron

The understanding of electron-solid interactions is of prime importance to both conventional transmission and scanning electron microscopists. Monte Carlo simulation of electron beam scattering in various target samples has made fundamental contributions to this understanding, especially in scanning electron microscopy where primary electron beams of a few to many kilovolts are utilized. A significant example is the understanding of energy dissipation patterns of incident electrons in an organic sample (polymethylmethacrylate-PMMA) which is very useful in electron beam lithography (1). Furthermore, with the close similarity between the organic sample and a biological specimen, a Monte Carlo approach is also very useful for the study of energy dissipation in biological specimens (2).To enhance our knowledge of these dissipation processes, a more comprehensive Monte Carlo simulation program has been developed. The program is based on the semi-direct technique of simulating individual inelastic scattering and elastic scattering with probabilistic weightings.

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Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100134922 ◽

1990 ◽

Vol 48 (2) ◽

pp. 264-265

Author(s):

D. R. Liu ◽

S. S. Shinozaki ◽

R. J. Baird

Keyword(s):

Thin Film ◽

Thin Films ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Compound Semiconductors ◽

Energy Dispersive Analysis ◽

X Ray ◽

Metastable Alloys ◽

Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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