In Vivo Proton Range Validation Using Pseudo Proton Radiography

The current clinical practice for Monte Carlo (MC) treatment planning reserves a 3.5% margin to compensate for proton range uncertainty. Additionally, patient positional uncertainty is typically 3-5 mm for proton craniospinal irradiation (CSI) treatment planning. These two uncertainties compromise the sparing of spine vertebrae in proton CSI patients. Computer tomography (CT) material characterization contributes approximately 2.5% proton range uncertainty. Multiple CT-tomaterial conversion methods have been investigated using dual-energy CT or magnetic resonance imaging to improve the range uncertainty. However, there is a lack of experimental data to validate the credibility of those material characterization models. We aim to develop an in vivo proton range method using pseudo proton radiography to validate imaging-based material characterization models consistently. Proton radiography techniques, such as proton water equivalent thickness (WET) and dose maps, were used to evaluate the in vivo proton range accuracy. Anteroposterior proton beams were penetrated through an anthropomorphic phantom. Then the exit doses were measured from proton radiography imaging. The validation experiment applied a newly designed multi-layer strip ionization chamber (MLSIC) for the first time to perform four-dimensional (4D) measurement for depth doses from 625 proton spots in two minutes. The depth doses of each spot were post-processed into WET imaging. A MatriXX PT was applied for 2D measurement from 19x19 cm2 proton fields. We compared the performance of the empirical DECT model and physics-informed machine learning (PIML) models for material conversion. The results indicated that the PIML-based material characteristic method generated more accurate WET and dose imaging using DECT compared to conventional machine learning and empirical material inference methods. The proposed in vivo proton range validation method can be used to quantify the credibility of DECT-based material conversion models for proton range enhancement. The method can potentially provide in-room patient anatomy changes to accomplish online adaption for modification. This technique will significantly benefit proton flash therapy, which demands high accuracy.