SU-F-BRD-06: Robust Dose Calculation in Intensity Modulated Proton Therapy

Purpose: Commissioning data for intensity modulated proton therapy (IMPT) must be post-processed by fits to ad-hoc functions to derive the dose calculation kernel parameters in a treatment planning system (TPS). Whether from experimental measurement or Monte Carlo simulation, the limited and noisy nature of such data makes this task very challenging. We present a method to improve the modeling of the lateral dose distribution of clinical energy proton beams in water to commission an in-house IMPT dose calculation engine. Methods: A linear sum of three Gaussian distribution functions was fitted to the lateral dose data in logarithmic scale. Starting values of fitting solutions were determined from the Generalized Highland Approximation. We exhaustively optimized the combinations of data weights with upper bounds of the fitting solutions to minimize confidence intervals of the fitting solutions while maintaining the coefficient of determination (R²). Results: Across all energies, average confidence bounds improved 72.88% [Max: 88.28%, Min: 55.05%] for small angle coulomb scattering, 114.25% [409.13%, 66.72%,] for nuclear scattering, and 68.66% [141.09%, 33.27%] for large angle coulomb scattering, while the coefficients of determination of the fits (R²) remained comparable. On average R ² only changed 0.18% and were very close to 1 (approx. 0.999). Wilcoxon signed rank tests comparing unweighted/unbounded fits with weighted/bounded fits averaged 0.0146 (Max: 0.177, Min: 7.05×10−⁷) for small angle Coulomb, 0.0903 (0.945, 7.05×10−⁷) for nuclear, and 0.254 (0.871, 1.86×10−⁶) for large angle Coulomb scattering. This allows rejection of the null hypothesis for small angle Coulomb scattering at the 0.015 level and nuclear interaction at the 0.1 level. Conclusion: Optimal weights assigned to IMPT lateral dose data minimized fitting to stochastic noise in the tail region. Optimizing the upper bounds of fitting parameters improved the robustness of fitting solutions. Both led to a more physically meaningful dose calculation kernel for the TPS. NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation