GPU-based fast Monte Carlo simulation for radiotherapy dose calculation
Center for Advanced Radiotherapy Technologies and Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92037-0843, USA
arXiv:1107.3355v1 [physics.med-ph] (18 Jul 2011)
@article{2011arXiv1107.3355J,
author={Jia}, X. and {Gu}, X. and {Jiang Graves}, Y. and {Folkerts}, M. and {Jiang}, S.~B.},
title={"{GPU-based fast Monte Carlo simulation for radiotherapy dose calculation}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1107.3355},
primaryClass={"physics.med-ph"},
keywords={Physics – Medical Physics},
year={2011},
month={jul},
adsurl={http://adsabs.harvard.edu/abs/2011arXiv1107.3355J},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
Monte Carlo (MC) simulation is commonly considered to be the most accurate dose calculation method in radiotherapy. However, its efficiency still requires improvement for many routine clinical applications. In this paper, we present our recent progress towards the development a GPU-based MC dose calculation package, gDPM v2.0. It utilizes the parallel computation ability of a GPU to achieve high efficiency, while maintaining the same particle transport physics as in the original DPM code and hence the same level of simulation accuracy. In GPU computing, divergence of execution paths between threads can considerably reduce the efficiency. Since photons and electrons undergo different physics and hence attain different execution paths, we use a simulation scheme where photon transport and electron transport are separated to partially relieve the thread divergence issue. High performance random number generator and hardware linear interpolation are also utilized. We have also developed various components to handle fluence map and linac geometry, so that gDPM can be used to compute dose distributions for realistic IMRT or VMAT treatment plans. Our gDPM package is tested for its accuracy and efficiency in both phantoms and realistic patient cases. In all cases, the average relative uncertainties are less than 1%. A statistical t-test is performed and the dose difference between the CPU and the GPU results is found not statistically significant in over 96% of the high dose region and over 97% of the entire region. Speed up factors of 69.1 ~ 87.2 have been observed using an NVIDIA Tesla C2050 GPU card against a 2.27GHz Intel Xeon CPU processor. For realistic IMRT and VMAT plans, MC dose calculation can be completed with less than 1% standard deviation in 36.1~39.6 sec using gDPM.
July 19, 2011 by hgpu
