Evaluation of Stopping-Power Prediction by Dual- and Single-Energy Computed Tomography in an Anthropomorphic Ground-Truth Phantom.

Abstract

To determine the accuracy of particle range prediction for proton and heavier ion radiation therapy based on dual-energy computed tomography (DECT) in a realistic inhomogeneous geometry and to compare it with the state-of-the-art clinical approach. A 3-dimensional ground-truth map of stopping-power ratios (SPRs) was created for an anthropomorphic head phantom by assigning measured SPR values to segmented structures in a high-resolution CT scan. This reference map was validated independently comparing proton transmission measurements with Monte Carlo transport simulations. Two DECT-based methods for direct SPR prediction via the Bethe formula (DirectSPR) and 2 established approaches based on Hounsfield look-up tables (HLUTs) were chosen for evaluation. The SPR predictions from the 4 investigated methods were compared with the reference, using material-specific voxel statistics and 2-dimensional gamma analysis. Furthermore, range deviations were analyzed in an exemplary proton treatment plan. The established reference SPR map was successfully validated for the discrimination of SPR and range differences well below 0.3% and 1 mm, respectively, even in complex inhomogeneous settings. For the phantom materials of larger volume (mainly brain, soft tissue), the investigated methods were overall able to predict SPR within 1% median deviation. The DirectSPR methods generally performed better than the HLUT approaches. For smaller phantom parts (such as cortical bone, air cavities), all methods were affected by image smoothing, leading to considerable SPR under- or overestimation. This effect was superimposed on the general SPR prediction accuracy in the exemplary treatment plan. DirectSPR predictions proved to be more robust, with high accuracy in particular for larger volumes. In contrast, HLUT approaches exhibited a fortuitous component. The evaluation of accuracy in a realistic phantom with validated ground-truth SPR represents a crucial step toward possible clinical application of DECT-based SPR prediction methods.