ArticlePhysics in medicine and biology2011
Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure.
Article in Physics in medicine and biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging.Biomedical physics & engineering express · 2024Article
- Scalable and accessible personalized photodynamic therapy optimization with FullMonte and PDT-SPACE.Journal of biomedical optics · 2022Article
- Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.International journal of molecular sciences · 2021Review
- HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.BMC medical informatics and decision making · 2020Article
- A Novel GPU-based Fast Monte Carlo Photon Dose Calculating Method for Accurate Radiotherapy Treatment Planning.Journal of biomedical physics & engineering · 2020Article
- Monte Carlo verification of radiotherapy treatments with CloudMC.Radiation oncology (London, England) · 2018Article
- Independent calculation of monitor units for VMAT and SPORT.Medical physics · 2015Article
- Toward a web-based real-time radiation treatment planning system in a cloud computing environment.Physics in medicine and biology · 2013Article
- Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce.Journal of biomedical optics · 2011Article
- Internet-based computer technology on radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation OncologyReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Monte Carlo (MC) methods are the gold standard for modeling photon and electron transport in a heterogeneous medium; however, their computational cost prohibits their routine use in the clinic. Cloud computing, wherein computing resources are allocated on-demand from a third party, is a new approach for high performance computing and is implemented to perform ultra-fast MC calculation in radiation therapy. We deployed the EGS5 MC package in a commercial cloud environment. Launched from a single local computer with Internet access, a Python script allocates a remote virtual cluster. A handshaking protocol designates master and worker nodes. The EGS5 binaries and the simulation data are initially loaded onto the master node. The simulation is then distributed among independent worker nodes via the message passing interface, and the results aggregated on the local computer for display and data analysis. The described approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The output of cloud-based MC simulation is identical to that produced by single-threaded implementation. For 1 million electrons, a simulation that takes 2.58 h on a local computer can be executed in 3.3 min on the cloud with 100 nodes, a 47× speed-up. Simulation time scales inversely with the number of parallel nodes. The parallelization overhead is also negligible for large simulations. Cloud computing represents one of the most important recent advances in supercomputing technology and provides a promising platform for substantially improved MC simulation. In addition to the significant speed up, cloud computing builds a layer of abstraction for high performance parallel computing, which may change the way dose calculations are performed and radiation treatment plans are completed.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.