Full Monte Carlo-based biological treatment plan optimization system for intensity modulated carbon ion therapy on GPU

Publication date: Available online 12 September 2017
Source:International Journal of Radiation Oncology*Biology*Physics
Author(s): Nan Qin, Chenyang Shen, Min-Yu Tsai, Marco Pinto, Zhen Tian, Georgios Dedes, Arnold Pompos, Steve B. Jiang, Katia Parodi, Xun Jia
PurposeOne of the major benefits of carbon ion therapy is enhanced biological effectiveness at the Bragg peak region. For intensity modulated carbon ion therapy (IMCT), it is desirable to employ Monte Carlo (MC) methods to compute properties of each pencil-beam spot for treatment planning, because of their accuracy in modeling physics processes and estimating biological effects. We have previously developed goCMC, a graphics processing unit (GPU)-oriented MC engine for carbon ion therapy. The purpose of this study is to build a biological treatment plan optimization system based on goCMC.Methods and MaterialsThe repair-misrepair-fixation model was implemented to compute spatial distribution of linear-quadratic model parameters for each spot. A treatment plan optimization module was developed to minimize the difference between the prescribed and actual biological effect. We employed a gradient-based algorithm to solve the optimization problem. The system was embedded in the Varian Eclipse treatment planning system under a client-server architecture to achieve a user-friendly planning environment. We tested the system with a 1-dimensional homogeneous water case and 3-dimensional patient cases.ResultsOur system generated treatment plans with biological spread-out Bragg peaks covering the targeted regions, while sparing critical structures. Using four NVidia GTX 1080 GPUs, total computation time including spot simulation, optimization, and final dose calculation was 0.6 hours for the prostate case (8282 spots), 0.2 hours for the pancreas case (3795 spots), and 0.3 hours for the brain case (6724 spots). The computation time was dominated by MC spot simulation.ConclusionsWe have built a biological treatment plan optimization system for IMCT that performs simulations by a fast MC engine, goCMC. To our knowledge, this is the first time that full MC-based IMCT inverse planning has been achieved in a clinically viable timeframe.



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