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Abstract
PURPOSE Analytical algorithms have limited accuracy when modeling very heterogeneous tumor sites. This work addresses the performance of a hybrid dose optimizer that combines both Monte Carlo (MC) and pencil beam (PB) dose engines to get the best outcome for proton therapy plans in terms of speed and accuracy. MATERIALS AND METHODS The hybrid optimization strategy calculates the optimal spot weights (w) using the analytical beamlets matrix (PPB) and a correction term C. After a first optimization where C = 0, the method alternates optimization of w using PPB with updates of C = DMC – DPB, where DMC results from a regular MC computation and DPB = PPB * w. Updates of C can be triggered as often as necessary by calling the MC with the last corrected weights w as input. The hybrid method was applied to three cases (prostate, lung and brain) and compared with full MC-based plans (PMC). For simplicity, PTV-based plans were created but the method can be equally applied in robust optimization. The plans were created with our in-house treatment planning system (TPS), which is coupled with a PB algorithm and a super-fast MC. RESULTS The MC recomputed doses after initial optimization (C=0, before correction) showed important dose degradation for all patients, with D95 below the clinical constraint (D95 > 95% of dose prescription, Dp) and significant overdose (D5 > 105% Dp). The hybrid method was able to recover excellent target coverage and reduced overdose after only a single update of C. The computation time of hybrid plans was reduced by a factor 7 w.r.t the full MC-based plans. CONCLUSIONS This study demonstrated the successful performance of hybrid MC-PB optimization for proton therapy, which can be immediately considered as an option for improving the dose calculation accuracy of commercial analytical TPS.
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Barragan Montero, A. M., Souris, K., Daniel Sanchez, Alejandro Carabe, Lee, J., & Sterpin, E. (2017). Performance of a hybrid Monte Carlo-Pencil beam dose algorithm for proton therapy. Medical Physics, 45(2), 846-862. https://hdl.handle.net/2078.5/173223 (Original work published 2017)