Purpose: Small and complex MLC apertures in modern IMRT and VMAT plans may lead to decreases in planned dose calculation accuracy. Monte Carlo (MC) is regarded as the most accurate method for dose calculation. However, its clinical adoption has been limited by the steep learning curve and limited availability of computing resources. The purpose of this study is to develop and validate a solution that addresses these issues.
Methods: MC package PENELOPE was modified to include Message-passing-interface (MPI) for parallel computing. Phase space file (PSF) from Varian was used. To reduce latent variation in the PSF, a rotational augmentation was performed. Transport in Jaws and MLCs were modeled with first-order approximation. An Amazon Machine Image was created for easy deploying and sharing. Client software was created to automate the entire independent dose calculation process from the request of cloud instance to the report generation. Validation tests such as open field, Dynamic leaf gap (DLG) were performed. 31 VMAT plans were used to evaluate the calculation time at 2% statistical uncertainty setting.
Results: Open field profiles, PDD, DLG and MLC transmission agrees well with the measurement. For clinical plans, most plans have discrepancy with planned dose of less than 2.5%. Only one lung and one spine plan has discrepancy at 3%. Possibly due to the different calculation grid used. MC dose with 2% statistical uncertainty can be computed around 5.5Â±2.6 minutes on c5n.18xlarge instances. The initialization of cloud instances, data preprocessing/transfer and report generation took additional 1.5, 0.7 and 1.6 minutes respectively. With spot instance request, average cloud computing cost is less than $0.2 per plan.
Conclusion: A cloud-based MC dose calculation tool was developed. It can be easily deployed to different clinics with little cost for computing resources.