Designing a therapy plan is a multi-dimensional optimization problem. The key factors which determine the success of EBT are correct and accurate treatment therapy planning and quality assurance procedures prior to delivery of the therapeutic dose. Įxternal photon beam therapy (EBT) is nowadays the most common cancer radiotherapy modality. The codes, training and test data, together with readout procedures, are freely available at the site. 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. 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. ResultsĪnalysed 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. These final estimates are then used to determine dose profiles in MC simulations. 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. Results of the regression are then used to reconstruct the dose profiles based on the PCA model. 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. The PCA-obtained features are then used by Support Vector Regressors to estimate the parameters of the model of the electron beam. The first model applies Principal Component Analysis to measured dose profiles in order to extract principal features of the shapes of the these profiles. Two regression models for estimating the parameters of the simulated primary electron beam, both based on machine learning, were developed. MethodsĪll simulations were run using PRIMO MC simulator. 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. 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. 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.
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