Inference of the optimal pretarget electron beam parameters in a Monte Carlo virtual linac model through simulated annealing

2009 ◽  
Vol 36 (6Part1) ◽  
pp. 2309-2319 ◽  
Author(s):  
Karl Bush ◽  
Sergei Zavgorodni ◽  
Wayne Beckham
Keyword(s):  
Monte Carlo ◽  
Electron Beam ◽  
Simulated Annealing ◽  
Beam Parameters

scholarly journals DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo software

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zbisław Tabor ◽  
Damian Kabat ◽  
Michael P. R. 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 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.

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

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

Decoupling initial electron beam parameters for Monte Carlo photon beam modelling by removing beam-modifying filters from the beam path

2005 ◽  
Vol 50 (24) ◽  
pp. 5935-5951 ◽  
Author(s):  
B De Smedt ◽  
N Reynaert ◽  
F Flachet ◽  
M Coghe ◽  
M G Thompson ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Electron Beam ◽  
Photon Beam ◽  
Beam Path ◽  
Initial Electron ◽  
Beam Parameters

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

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.

PO-1352: Prediction of electron beam parameters of a Monte Carlo model using machine learning

2020 ◽  
Vol 152 ◽  
pp. S716-S717
Author(s):  
A. Wagner ◽  
K. Brou Boni ◽  
E. Rault ◽  
F. Crop ◽  
T. Lacornerie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Electron Beam ◽  
Monte Carlo Model ◽  
Beam Parameters

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

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.

Influence of initial electron beam parameters on Monte Carlo calculated absorbed dose distributions for radiotherapy photon beams

2004 ◽  
Vol 31 (4) ◽  
pp. 907-913 ◽  
Author(s):  
Antonis Tzedakis ◽  
John E. Damilakis ◽  
Michael Mazonakis ◽  
John Stratakis ◽  
Haralambos Varveris ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Electron Beam ◽  
Absorbed Dose ◽  
Photon Beams ◽  
Dose Distributions ◽  
Initial Electron ◽  
Beam Parameters

scholarly journals Benchmarking of electron beam parameters based on Monte Carlo linear accelerator simulation

2020 ◽  
Vol 9 (2) ◽  
pp. 577-584
Author(s):  
Fan Zhang ◽  
Mi Zhou ◽  
Jing Liu ◽  
Lu Yue ◽  
Lihua Deng ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Electron Beam ◽  
Linear Accelerator ◽  
Beam Parameters
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