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Automatic determination of primary electron beam parameters in Monte Carlo simulation
Javier Pena1, Diego M González-Castaño, Faustino Gómez
1Departamento de Física de Partículas, Facultade de Física, Universidade de Santiago de Compostela, Spain. javierpg@usc.es
This study introduces a new method for Monte Carlo simulations of medical linear accelerator (linac) photon beams. It accurately reproduces clinical fields by optimizing primary electron beam parameters, ensuring reliable simulations.
Area of Science:
- Medical Physics
- Computational Dosimetry
- Radiotherapy Physics
Background:
- Accurate Monte Carlo simulations are crucial for medical linear accelerator (linac) photon beam modeling.
- Precise determination of primary electron beam parameters is fundamental for reliable simulations.
Purpose of the Study:
- To propose and validate a novel methodology for commissioning photon beams in Monte Carlo simulations.
- To ensure the reproducibility of clinically relevant field sizes and characteristics.
Main Methods:
- Accelerated Monte Carlo simulations were performed for various field sizes (2x2, 10x10, 20x20 cm²) at a source-surface distance of 100 cm.
- Simulations explored combinations of primary electron beam mean energy and radial full width at half maximum (FWHM).
- A simultaneous comparison with experimental measurements identified the optimal electron beam parameters.
Main Results:
- The proposed methodology successfully reproduced clinical photon beam characteristics for Siemens PRIMUS and Varian 2100 CD machines.
- Excellent agreement was achieved between simulated and measured data across a wide range of field sizes.
- The commissioning process was fully automated using precalculated profiles stored in databases.
Conclusions:
- The developed methodology provides an accurate and automated approach for Monte Carlo simulation commissioning of medical linac photon beams.
- The database-driven approach ensures reproducibility and facilitates characterization of accelerators across different sites.
- This method enhances the reliability of Monte Carlo simulations for radiotherapy applications.
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