Proton therapy range uncertainty reduction using vendor-agnostic tissue characterization on a virtual photon-counting...

Photon-counting CT (PCCT) is the latest technology enabling imaging with reduced noise and inherent spectral separation, with the potential to directly calculate a more accurate tissue stopping power from spectral data. This potential benefit is difficult to quantify in practice and is currently evaluated mainly in phantoms with simplified geometries that only approximate real patient anatomy.

In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a CT system. In addition, we validate the accuracy of stopping power ratio (SPR) calculations on a model of a PCCT scanner using a conventional stoichiometric calibration approach and a prototype software TissueXplorer.

A validated CT simulator (DukeSim) was used to generate PCCT projections of a computational head phantom, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors. The ground-truth treatment plan was made directly on the computational phantom using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The resulting dose distributions were subsequently compared against the ground-truth plan to quantify dose differences arising from SPR estimation.

The mean percentage difference in estimating the SPR with TissueXplorer in all head tissues inside the scanned volume was 0.28%. SPRs obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head phantom.

Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information hold promise for more accurate prediction of the SPR than the conventional stoichiometric approach.