Robust Optimizer for Intensity Modulated Proton Therapy based on GPU

XU Yao; PEI Xi; LIU Hongdong; HUO Wanli; ZHOU Jieping; XU Xie

doi:10.11804/NuclPhysRev.36.01.096

This paper describes the development of a fast robust optimization tool that takes advantage of the GPU technologies. The objective function of the robust optimization model considered nine boundary dose distributions——two for ±range uncertainties, six for ±set-up uncertainties along anteroposterior (A-P), lateral (R-L) and superior{inferior (S-I) 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-one head and neck cancer case, one lung cancer case and one prostate cancer case-were investigated to demonstrate the clinical significance of the proposed robust optimizer. Compared with conventional planning target volume (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 around 10 s for 100 iterations. 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.

Robust Optimizer for Intensity Modulated Proton Therapy based on GPU

1. School of Physical Sciences, University of Science and Technology of China, Hefei 230026, China;

2. The First Affiliated Hospital of USTC West District, Hefei 230031, China;

3. Rensselaer Polytechnic Institute, Troy 12180, USA

Funds: National Key R&D Program of China (2017YFC0107500); National Natural Science Foundation of China (11575180); Anhui Provincial Key R&D Program (1804a09020039); Anhui Provincial Natural Science Foundation (1908085MA27)

Received Date: 2018-12-12

Rev Recd Date: 2019-02-26

Publish Date: 2019-03-20

Key words:

Abstract: This paper describes the development of a fast robust optimization tool that takes advantage of the GPU technologies. The objective function of the robust optimization model considered nine boundary dose distributions——two for ±range uncertainties, six for ±set-up uncertainties along anteroposterior (A-P), lateral (R-L) and superior{inferior (S-I) 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-one head and neck cancer case, one lung cancer case and one prostate cancer case-were investigated to demonstrate the clinical significance of the proposed robust optimizer. Compared with conventional planning target volume (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 around 10 s for 100 iterations. 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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Robust Optimizer for Intensity Modulated Proton Therapy based on GPU

doi: 10.11804/NuclPhysRev.36.01.096

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