The ray-tracing algorithm can reliably replace the TPS method in MLIC PR for in vivo range verification and it can be a key component to develop software tools for spatial alignment and correction of CT calibration.

Validation of the proton range accuracy and optimization of CT calibration curves utilizing range probing. Physics in Medicine and Biology, 65(3), [03NT02].

For registration of proton radiography data with X-ray CT, the use of a direct ray-tracing algorithm to compute sums of squared differences and corrections of range errors showed very good accuracy and robustness with respect to three confounding factors: measurement noise, calibration error, and spot spacing. It is therefore a suitable algorithm to use in the in vivo range verification framework, allowing to separate in postprocessing the proton range uncertainty due to setup errors from the other sources of uncertainty.

Purpose In proton therapy, the conversion of the planning computed tomography (CT) into proton stopping powers is tainted by uncertainties which may jeopardize dose conformity. Proton radiography provides a direct information on the energy reduction of protons in the patient. However, it is currently limited by the degradation (“blurring”) of the one‐dimensional depth‐dose deposition profiles which constitute the pixels. Methods An iterative algorithm is implemented to extract high‐resolution water equivalent thickness (WET) maps from the measurements of depth‐dose profiles acquired with a multilayer ionization chamber. The method relies on the assumption that those curves are a function of the WET, which can benefit from a sparse representation. Results When used without relying on any prior knowledge derived from the planning CT, the method already outperforms the published one in terms of accuracy. We also propose a variant which integrates the planning CT in a robust fashion to further improve the deconvolution result and reach an accuracy of 1.5 mm on the estimated WET. The methods are applied to both synthetic data and actual proton radiography acquisitions on phantoms. Conclusions Besides the increase in accuracy achieved in the estimation of WET maps from proton radiography data, we demonstrate that the proposed deconvolution algorithm is also more robust with respect to confounding factors such as residual setup errors or changes in the anatomy. Therefore, proton radiography using a range probe provides both the required accuracy to assess and reduce range uncertainty in proton therapy and the simplicity of integrated‐mode proton radiography.