The efficacy of proton beam therapy is limited by stopping power ratio (SPR) prediction uncertainties in patient tissues. This study compared image artefacts and SPR prediction accuracy across a single-energy computed tomography (SECT) and three dual-energy computed tomography (DECT) workflows: SECT with a clinical Hounsfield look-up table (HLUT), two commercial DECT algorithms (DirectSPR and MMSim), and an in-house developed model applied to material density (MD) images, called MD-SPR. SPR images of a head-sized phantom with 24 inserts of tissue surrogate and non-tissue materials were evaluated for image artefacts and compared with measured reference SPRs of the inserts. The root-mean-square SPR differences for tissue surrogates were 0.011 (HLUT), 0.005 (DirectSPR), 0.007 (MMSim), and 0.005 (MD-SPR). For non-tissue materials, the differences were 0.167, 0.028, 0.034, and 0.011, respectively. These results indicate that DECT-based SPR prediction workflows, particularly MD-SPR, can reduce both image artefacts and range uncertainties, compared with a SECT-based HLUT workflow.
Pettersson et al. (Sat,) studied this question.