Material assignment for proton range prediction in Monte Carlo patient simulations using stopping-power datasets

Motivation and objective. For each institute, the selection and calibration of the most suitable approach to assign material properties for Monte Carlo (MC) patient simulation in proton therapy is a major challenge. Current conventional approaches based on computed tomography (CT) depend on CT acquisition and reconstruction settings. This study proposes a material assignment approach, referred to as MATA (MATerial Assignment), which is independent of CT scanner properties and, therefore, universally applicable by any institute.

The MATA approach assigns material properties to the physical quantity stopping-power ratio (SPR) using a set of 40 material compositions specified for human tissues and linearly determined mass density. The application of clinically available CT-number-to-SPR conversion avoids the need for any further calibration. The MATA approach was validated with homogeneous and heterogeneous SPR datasets by assessing the SPR accuracy after material assignment obtained either based on dose scoring or determination of water-equivalent thickness. Finally, MATA was applied on patient datasets to evaluate dose differences induced by different approaches for material assignment and SPR prediction.

The deviation between the SPR after material assignment and the input SPR was close to zero in homogeneous datasets and below 0.002 (0.2% relative to water) in heterogeneous datasets, which was within the systematic uncertainty in SPR estimation. The comparison of different material assignment approaches revealed relevant differences in dose distribution and SPR. The comparison between two SPR prediction approaches, a standard look-up table and direct SPR determination from dual-energy CT, resulted in patient-specific mean proton range shifts between 1.3 mm and 4.8 mm.

MATA eliminates the need for institution-specific adaptations of the material assignment. It allows for using any SPR dataset and thus facilitates the implementation of more accurate SPR prediction approaches. Hence, MATA provides a universal solution for patient modeling in MC-based proton treatment planning.