Performance optimization in 4D radiation treatment planning using Monte Carlo simulation on the cloud

Article type: Research Article

Authors: Chow, James C.L.^{a; b}

Affiliations: [a] Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada | [b] Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, ON, Canada. E-mail: james.chow@rmp.uhn.on.ca

Abstract: Performance of 4D radiation treatment planning using Monte Carlo simulation on the cloud was evaluated with optimizations based on the number of compute nodes, number of computed tomography (CT) image sets and dose reconstruction time. The dose distribution of a lung 4D treatment plan considering motion in free breathing was calculated by the EGSnrc-based Monte Carlo code. The plan was created by the DOSCTP linked to the cloud and calculated results were sent to the FFD4D for dose reconstruction. The dependence of treatment plan computing time on the number of compute nodes was evaluated with variations of the number of CT image sets and dose reconstruction time. It is found that the dependence of computing time on the number of nodes was affected by the diminishing return of the number of nodes in Monte Carlo simulation. Moreover, effects of the number of CT image sets and dose reconstruction time were found insignificant when the number of compute nodes was larger than 15 on the cloud. It is concluded that the optimized number of compute nodes selected in simulation should be between 5 and 15, in which the dependence of computing time on the number of nodes is significant.

Keywords: Could computing, Monte Carlo simulation, 4D treatment planning

DOI: 10.3233/JCM-150592

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 16, no. 1, pp. 147-156, 2016