Particle tracking, recognition and LET evaluation of out-of-field proton therapy delivered to a phantom with implants.

Abstract

This study aims to assess the composition of scattered particles generated in proton therapy for tumors situated proximal to titanium dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumor.
Approach: A therapeutic conformal proton beam was used to deliver the treatment plan to an anthropomorphic head phantom with two types of implants inserted in the target volume (made of titanium and plastic, respectively). The scattered radiation resulted during the irradiation was detected by a hybrid semiconductor pixel detector MiniPIX Timepix3 that was placed distal to the Spread-out Bragg peak. Visualization and field decomposition of stray radiation were generated using algorithms trained in particle recognition based on artificial intelligence convolution neural networks (AI CNN). Spectral sensitive aspects of the scattered radiation were collected using two angular positions of the detector relative to the beam direction: 0° and 60°.
Results: Using AI CNN, 3 classes of particles were identified: protons, electrons & photons, and ions & fast neutrons. Placing a Titanium implant in the beam's path resulted in predominantly electrons and photons, contributing 52.2%, whereas for plastic implants, the contribution was 65.4%. Scattered protons comprised 45.5% and 31.9% with and without metal inserts, respectively. The LET spectra was derived for each group of particles identified, with values ranging from 0.01 to 7.5 keV·μm-1 for Titanium implants/plastic implants. The low-LET component was primarily composed of electrons and photons, while the high-LET component corresponded to protons and ions.
Significance: This method, complemented by directional maps, holds potential for evaluating and validating treatment plans involving stray radiation near organs at risk, offering precise discrimination of the mixt field, enhancing in this way the LET calculation. &#xD.