Validation and testing of a novel pencil-beam model derived from Monte Carlo simulations in carbon-ion treatment planning for different scenarios

Purpose: The calculation ability of the newly-proposed accurate beam model, the double Gaussian-logistic (DG-L) model, was validated in both homogeneous and heterogeneous phantoms to provide helpful information for its future application in clinical carbon-ion treatment planning system (TPS). Methods: MatRad was used as the new algorithm test platform. Based on Monte Carlo (MC) method, the basic database in matRad was generated, then comparative dosimetric analyses between the single Gaussian (SG), double Gaussian (DG) and DG-L models against the MC recalculations were performed on the treatment plans of a cubic water phantom, a TG119 phantom and a liver patient scenario. Absolute dose differences, dose-volume histograms (DVHs) and global gamma-index analyses derived from the treatment plans were evaluated. Results: Calculated with the DG-L model, the deviations of the target dose coverage (D95) for the cubic water phantom, the TG119 phantom and the liver patient case against the MC recalculations could be reduced from-2.5%,-4.6% and-6.4% to-0.3%,-2.0% and-4.5% respectively compared to the SG model, while the gamma pass rates (3%/3mm) could be enhanced from 98.0%, 90.6% and 90.1% to 99.8%, 95.7% and 91.6%, respectively. The novel beam model also shows improved performance compared with the DG model, without substantially increasing the computation time. Conclusions: The DG-L model could effectively improve the dose calculation accuracy and mitigate the delivered dose deficiency in target volumes compared to the SG and DG models. The lateral heterogeneities should be considered for its future implementation in a clinical TPS.

Automated summary

The study validates the calculation accuracy of the newly-proposed double Gaussian-logistic (DG-L) model in both homogeneous and heterogeneous phantoms, providing useful information for its future application in clinical carbon-ion treatment planning system.