Prompt-Gamma Spatial Energy-Distribution in a Proton Therapy Plan for Beam Range Verification

Abstract

Generating a 2D energy-spatial distribution of the Prompt Gamma emission through FLUKA Monte Carlo simulations (from a proton therapy plan developed in a treatment planning system). The PG 2D spatial distribution can be potentially included in the Treatment Planning System (TPS), to predict the proton beam range and fine tune the dose distributions. A breast proton plan was developed in the treatment planning system, and then simulated in FLUKA MC. The patient geometry in FLUKA consisted of the voxelized CT, where the clinical dose was scored. The incident proton beam was modelled according to (Fiorini, 2018). A PG slit Energy-Dispersive Pixelated (EPD) imaging detector (Vella, 2020) was added to the model at 90 degrees with the respect to gantry 0. The EPD detector is capable of simultaneously capturing energy-spectra and images of the gamma-ray emission. Pixelated images are captured at the detector through the slit (60mm thickness, 10mm aperture). Two PG energy-spatial distribution were estimated, one at the target from the secondary PG generated by the proton incident beams, and the other tracked from the target to the PG detector through the slit collimator. The two energy-distributions were compared to assess the efficiency of the detector and estimate the proton range. A 2D image of the beam was generated from the proton plan to assess the detector physical capabilities. The optimum number of PGs required to generate a 2D spatial distribution of the beam in water through a slit collimator is ∼106 (Vella, 2020). However, detecting the PG emission through the slit has 1% efficiency. The number of incident protons of the beam required to generate a PG image of the beam to assess the proton range should be at least 1010, which is equivalent to a PG spatial distribution of ∼108 in the tumor. Of those, 106 gamma-rays will then reach the detector through the slit, the emission at the maximum dose. Variance reduction techniques were applied to improve the efficiency of the simulations. Finally, proton range from the PG energy-spatial distribution was estimated and compared to the proton dose distribution which were found to be within 2% difference. We have shown that the prompt-gamma secondary emission from a proton treatment can generate a 2D energy-spatial distribution of the proton beam able to assess the proton range. The optimum number of PG necessary to generate a beam image was estimated. Energy-spatial distribution of the proton induced PG emission can be potentially included in the TPS to predict the beam range and fine tune the dose delivery plan. The next step is exploring alternative detector/collimator combination to improve the efficiency of the imaging system.