Fifty-seventh annual meeting of the American association of physicists in medicine
SU-E-T-681: Robust and Monte Carlo-Based Intensity Modulated Proton Therapy Optimization with GPU Acceleration
Accuracy of dose calculation models and robustness under various uncertainties are key factors influencing the quality of intensity modulated proton therapy (IMPT) plans. In this work, a robust IMPT optimization based on accurate Monte Carlo (MC) dose calculation is developed.
We used an in-house developed and graphics processing unit (GPU) accelerated MC for dose calculation. For robust optimization, dose volume histograms (DVHs) were computed for each uncertainty scenario at each optimization iteration. A gradient based adaptive method was used to improve the DVHs with adjustable scenario weights. GPUs were employed to accelerate the optimization process. Uncertainties in patient setup and proton range were considered in all cases studied. Additionally, the uncertainty of intra-fraction relative shift between fields was considered for craniospinal irradiation cases. The adaptive robust optimization method was compared with for clinical cases at several different disease sites.
Comparing with the traditional optimization target volume (OTV) based method, the adaptive robust optimization spared critical structures better while maintain the target coverage in clinical cases. For example, the right parotid hot spot dose was reduced from 78.5Gy to 74.5Gy as shown in Fig. 1. For craniospinal irradiation, the adaptive method found the robust solution at field junctions without manual feathering of the match lines. Even for relatively large head-and-neck cases and craniospinal cases, the whole process of MC dose calculation and robust optimization can be done within 30 minutes on a system of 100 Nvidia GeForce GTX Titan cards.
A robust IMPT treatment planning system is developed utilizing an adaptive method. The treatment planning optimization is based on MC dose calculation and is accelerated by GPU to be clinically viable.
This work is supported in part by Varian Medical Systems.