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Program Information

Proton Source Modeling for Geant4 Monte Carlo Simulations

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S Barnes

S Barnes1*, G McAuley1, A Wroe2, J Slater1, (1) Loma Linda University, Loma Linda, CA, (2) Loma Linda University Medical Center, Loma Linda, CA

SU-E-T-232 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall

Purpose: To investigate the effect of initial proton beam source placement, distribution and angle on the proton dose distribution in a therapeutic nozzle using Geant4.

Methods: We performed Geant4 Monte Carlo simulations of a passively scattered proton treatment nozzle. Accurate geometry including all elements in the treatment room was used. Protons were generated just inside the vacuum pipe using one of two models. First, a standard two dimensional Gaussian distribution of proton starting position was used with a small random angle added to the initial direction. The size of the Gaussian distribution and the random angle were set to match measured beam spot size and angular spread at the exit window. Second, a point source of protons further back in the vacuum pipe with a small random angle was used. The distance of the point source to exit window and the random angle were set to match the spot size and angular deviation used for the Gaussian distribution. Depth dose curves and orthogonal beam profiles were examined to determine changes between the two models.

Results: Orthogonal beam profiles for large apertures showed changes of up to 6.5% between the two models with the point source showing much better agreement with measured data. Depth dose curves and orthogonal profiles for small apertures were unaffected. For large apertures, the average difference compared to measured data was of 1.9% and 0.7% and the max difference was 5.0% and 1.6% for Gaussian and point sources, respectively.

Conclusions: The point source more realistically models the proton distribution in the vacuum pipe by correlating the proton position with the direction. For certain scattering setups and large apertures point source modeling is necessary to accurate match measured data with Monte Carlo simulations.

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