Publication:
Monte Carlo verification of radiotherapy treatments with CloudMC.

dc.contributor.authorMiras, Hector
dc.contributor.authorJimenez, Ruben
dc.contributor.authorPerales, Alvaro
dc.contributor.authorTerron, Jose Antonio
dc.contributor.authorBertolet, Alejandro
dc.contributor.authorOrtiz, Antonio
dc.contributor.authorMacias, Jose
dc.contributor.funderFundación Progreso y Salud
dc.contributor.funderConsejería de Salud de la Junta de Andalucía
dc.date.accessioned2023-01-25T10:20:35Z
dc.date.available2023-01-25T10:20:35Z
dc.date.issued2018-06-27
dc.description.abstractA new implementation has been made on CloudMC, a cloud-based platform presented in a previous work, in order to provide services for radiotherapy treatment verification by means of Monte Carlo in a fast, easy and economical way. A description of the architecture of the application and the new developments implemented is presented together with the results of the tests carried out to validate its performance. CloudMC has been developed over Microsoft Azure cloud. It is based on a map/reduce implementation for Monte Carlo calculations distribution over a dynamic cluster of virtual machines in order to reduce calculation time. CloudMC has been updated with new methods to read and process the information related to radiotherapy treatment verification: CT image set, treatment plan, structures and dose distribution files in DICOM format. Some tests have been designed in order to determine, for the different tasks, the most suitable type of virtual machines from those available in Azure. Finally, the performance of Monte Carlo verification in CloudMC is studied through three real cases that involve different treatment techniques, linac models and Monte Carlo codes. Considering computational and economic factors, D1_v2 and G1 virtual machines were selected as the default type for the Worker Roles and the Reducer Role respectively. Calculation times up to 33 min and costs of 16 € were achieved for the verification cases presented when a statistical uncertainty below 2% (2σ) was required. The costs were reduced to 3-6 € when uncertainty requirements are relaxed to 4%. Advantages like high computational power, scalability, easy access and pay-per-usage model, make Monte Carlo cloud-based solutions, like the one presented in this work, an important step forward to solve the long-lived problem of truly introducing the Monte Carlo algorithms in the daily routine of the radiotherapy planning process.
dc.description.versionSi
dc.identifier.citationMiras H, Jiménez R, Perales Á, Terrón JA, Bertolet A, Ortiz A, et al. Monte Carlo verification of radiotherapy treatments with CloudMC. Radiat Oncol. 2018 Jun 27;13(1):99.
dc.identifier.doi10.1186/s13014-018-1051-9
dc.identifier.essn1748-717X
dc.identifier.pmcPMC6020449
dc.identifier.pmid29945681
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020449/pdf
dc.identifier.unpaywallURLhttps://doi.org/10.1186/s13014-018-1051-9
dc.identifier.urihttp://hdl.handle.net/10668/12644
dc.issue.number1
dc.journal.titleRadiation oncology (London, England)
dc.journal.titleabbreviationRadiat Oncol
dc.language.isoen
dc.organizationInstituto de Biomedicina de Sevilla-IBIS
dc.organizationHospital Universitario Virgen Macarena
dc.page.number9
dc.provenanceRealizada la curación de contenido 17/06/2025.
dc.publisherBioMed Central Ltd.
dc.pubmedtypeJournal Article
dc.relation.projectIDPI-0261-2014
dc.relation.projectIDPIN-0215-2017
dc.relation.publisherversionhttps://ro-journal.biomedcentral.com/articles/10.1186/s13014-018-1051-9
dc.rightsAttribution 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCloud computing
dc.subjectMonte Carlo
dc.subjectRadiotherapy
dc.subject.decsRadioterapia
dc.subject.decsIncertidumbre
dc.subject.decsSoluciones
dc.subject.decsAlgoritmos
dc.subject.decsDosificación
dc.subject.meshAlgorithms
dc.subject.meshCloud Computing
dc.subject.meshHumans
dc.subject.meshMonte Carlo Method
dc.subject.meshPhantoms, Imaging
dc.subject.meshRadiometry
dc.subject.meshRadiotherapy Dosage
dc.subject.meshRadiotherapy Planning, Computer-Assisted
dc.subject.meshSoftware
dc.titleMonte Carlo verification of radiotherapy treatments with CloudMC.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number13
dspace.entity.typePublication

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
PMC6020449.pdf
Size:
847.42 KB
Format:
Adobe Portable Document Format