LET spectra scoring for applications in proton radiotherapy

Purpose: This work aims at implementation, validation, and proof of application of fast, voxel-based single proton linear energy transfer (LET) spectra scoring for clinical proton therapy. The LET spectra provide more comprehensive information on the mixed radiation field produced by protons in heterogeneous patient geometry in comparison to the dose-averaged LET (LET_d), commonly investigated pre-clinically and clinically. Materials and methods: We implemented single particle spectra scoring methods for LET and other physics quantities, e.g. deposited energy or track length, characterising mixed radiation fields in voxelized geometries. The scorers were implemented in a GPU-accelerated Monte Carlo (MC) code FRED, as well as a general purpose MC codes GATE/GEANT4 and FLUKA. The validation included a comparison of spectra obtained with FRED, GATE and FLUKA, and evaluating the calculation performance. The LET spectra were also calculated for an intensity-modulated proton therapy (IMPT) patient treatment plan and compared to LET_d. Results: Implementation of spectra scorers of various quantities, including the LET was shown to be conducted accurately and spectra obtained with FRED, GATE and FLUKA are in excellent agreement. The GPU acceleration allows precise and time-efficient calculation of the LET spectra and can be conducted within about an hour with FRED (using two GPU cards) compared to tens of hours with GATE or FLUKA (using up to 400 CPUs). We have also shown that for an IMPT patient treatment plan, single particle LET spectra may differ for the same LET_d, being potentially responsible for uncertainties in the advanced treatment planning methods based on variable RBE and LET_d. Conclusions: Single particle LET spectra scoring is possible with the state-of-the-art Monte Carlo methods, allowing a more detailed insight into radiation effects in mixed radiation fields produced by proton beams in a human body and, thanks to time efficient calculations enables future clinical translation of the method.