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Adaptive Robust Optimizer for Intensity Modulated Proton Therapy Based On OpenMP and GPU

J Chen1*, X Pei2 , Y Yin1 , (1) Shandong Cancer Hospital & Institute, Shandong Cancer Hospital affiliate to Shandong University, Jinan City, ,(2) University of Science and Technology of China, Hefei, 34


(Sunday, 7/14/2019)  

Room: ePoster Forums

Purpose: Robust optimization has been shown to be effective for stabilizing IMPT plans although the computing process is time-consuming. GPU has been used to speedup proton dose calculations. This paper describes the development of a fast robust optimization tool that takes advantage of the GPU.

Methods: The objective function of robust optimization model considered nine boundary dose distributions¾ two for ±range uncertainties, six for ±set-up uncertainties along anteroposterior, lateral and superior–inferior directions, and one for nominal situation. The nine boundary influence matrices were calculated using an in-house dose engine for proton pencil beams of a finite size, while the conjugate gradient method was applied to minimize the objective function. The GPU platform was adopted to accelerate both the proton dose calculation algorithm and the conjugate gradient method. Three clinical cases ¾ one head and neck cancer case, one lung cancer case and one prostate cancer case ¾ were investigated to demonstrate the clinical significance of the proposed robust optimizer.

Results: Compared with conventional PTV based IMPT plans, the proposed method was found to be conducive in designing robust treatment plans that were less sensitive to range and setup uncertainties. The three cases showed that targets could achieve high dose uniformity while organs at risks (OARs) were under better protection against setup and range errors. The run times for the three cases were less than 3 minutes for 100 iterations. In comparison with Eclipse 13.5 which did not support GPU acceleration, the proposed optimizer generated better dose distributions for all of cases. The run-time of CPU version was close to Eclipse while the GPU version outperformed Eclipse significantly.

Conclusion: The GPU-based fast robust optimizer developed in this study can serve to improve the reliability of traditional proton treatment planning by achieving a high level of robustness in a much shorter time.


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