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

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

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.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01847-w.