An OpenCL-based Monte Carlo dose calculation engine (oclMC) for coupled photon-electron transport

Zhen Tian, Feng Shi, Michael Folkerts, Nan Qin, Steve B. Jiang, Xun Jia
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
arXiv:1503.01722 [physics.med-ph], (5 Mar 2015)


   title={An OpenCL-based Monte Carlo dose calculation engine (oclMC) for coupled photon-electron transport},

   author={Tian, Zhen and Shi, Feng and Folkerts, Michael and Qin, Nan and Jiang, Steve B. and Jia, Xun},






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Monte Carlo (MC) method has been recognized the most accurate dose calculation method for radiotherapy. However, its extremely long computation time impedes clinical applications. Recently, a lot of efforts have been made to realize fast MC dose calculation on GPUs. Nonetheless, most of the GPU-based MC dose engines were developed in NVidia CUDA environment. This limits the code portability to other platforms, hindering the introduction of GPU-based MC simulations to clinical practice. The objective of this paper is to develop a fast cross-platform MC dose engine oclMC using OpenCL environment for external beam photon and electron radiotherapy in MeV energy range. Coupled photon-electron MC simulation was implemented with analogue simulations for photon transports and a Class II condensed history scheme for electron transports. To test the accuracy and efficiency of our dose engine oclMC, we compared dose calculation results of oclMC and gDPM, our previously developed GPU-based MC code, for a 15 MeV electron beam and a 6 MV photon beam on a homogenous water phantom, one slab phantom and one half-slab phantom. Satisfactory agreement was observed in all the cases. The average dose differences within 10% isodose line of the maximum dose were 0.48-0.53% for the electron beam cases and 0.15-0.17% for the photon beam cases. In terms of efficiency, our dose engine oclMC was 6-17% slower than gDPM when running both codes on the same NVidia TITAN card due to both different physics particle transport models and different computational environments between CUDA and OpenCL. The cross-platform portability was also validated by successfully running our new dose engine on a set of different compute devices including an Nvidia GPU card, two AMD GPU cards and an Intel CPU card using one or four cores. Computational efficiency among these platforms was compared.
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