Dose-weighted proton linear energy transfer map generation using a deep learning framework

The advantage of proton therapy over photon therapy lies in the Bragg peak effect, which allows protons to deposit most of their energy precisely at the tumor site, minimizing damage to surrounding healthy tissue. Despite this, the standard approach to clinical treatment planning does not fully consider the differences in biological effectiveness between protons and photons. Currently, a uniform Relative Biological Effectiveness (RBE) value of 1.1 is used in clinical settings to compare protons to photons, despite evidence that proton RBE can vary significantly. This variation underscores the need for more refined proton therapy treatment planning those accounts for the variable RBE. A critical parameter in assessing the RBE of proton therapy is the Dose-Average Linear Energy Transfer (LET_d), which is instrumental in optimizing proton treatment plans. Accurate LET_d distribution calculations require complex physical models and the implementation of sophisticated Monte-Carlo (MC) simulation software. These simulations are both computationally intensive and time-consuming. To address these challenges, we propose a Deep Learning (DL)-based framework aimed at predicting the LETd distribution map from the dose distribution map. This framework utilizes Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Normalized Cross Correlation (NCC) to measure discrepancies between MC-derived LETd and the LETd maps generated by our model. Our approach has shown promise in producing synthetic LET_d maps from dose maps, potentially enhancing proton therapy planning through the provision of precise LET_d information. This development could significantly contribute to more effective and individualized proton therapy treatments, optimizing therapeutic outcomes while further minimizing harm to healthy tissue.