Monte Carlo verification of radiotherapy treatments with CloudMC
Background A 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...
| Autores: | , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad de Navarra |
| Repositorio: | Dadun. Depósito Académico Digital de la Universidad de Navarra |
| Idioma: | inglés |
| OAI Identifier: | oai:dadun.unav.edu:10171/57273 |
| Acceso en línea: | https://hdl.handle.net/10171/57273 |
| Access Level: | acceso abierto |
| Palabra clave: | Cloud computing Monte Carlo Radiotherapy |
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Monte Carlo verification of radiotherapy treatments with CloudMCMacías, J. (José)|||/items/b57af471-bd60-4847-aae4-784788358314Ortiz, A. (Antonio)|||/items/7e712154-e56a-4d40-bd68-a4483b12f3bdBertolet, A. (Alejandro)|||/items/869bcc8b-6bfc-4d1d-ba5d-0ad346750671Terrón, J.A. (José Antonio)|||/items/2c9e1132-e349-4bc2-a919-76fe1720c114Perales-Molina, A. (Álvaro)|||/items/8b1124b9-84dd-4c60-80f9-2597148e65f4Jiménez, R. (Rubén)|||/items/a903f655-a81e-430d-8e56-46e9917b5b2dMiras, H. (Héctor)|||/items/c071a515-a18a-4ba0-9bac-b5846f73dbadCloud computingMonte CarloRadiotherapyBackground A 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. Methods 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. Results 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%. Conclusions 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.Dadun. Depósito Académico Digital Universidad de Navarra20192019-06-0420182018-01-0120182018-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/57273reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/572732026-06-21T12:47:57Z |
| dc.title.none.fl_str_mv |
Monte Carlo verification of radiotherapy treatments with CloudMC |
| title |
Monte Carlo verification of radiotherapy treatments with CloudMC |
| spellingShingle |
Monte Carlo verification of radiotherapy treatments with CloudMC Macías, J. (José)|||/items/b57af471-bd60-4847-aae4-784788358314 Cloud computing Monte Carlo Radiotherapy |
| title_short |
Monte Carlo verification of radiotherapy treatments with CloudMC |
| title_full |
Monte Carlo verification of radiotherapy treatments with CloudMC |
| title_fullStr |
Monte Carlo verification of radiotherapy treatments with CloudMC |
| title_full_unstemmed |
Monte Carlo verification of radiotherapy treatments with CloudMC |
| title_sort |
Monte Carlo verification of radiotherapy treatments with CloudMC |
| dc.creator.none.fl_str_mv |
Macías, J. (José)|||/items/b57af471-bd60-4847-aae4-784788358314 Ortiz, A. (Antonio)|||/items/7e712154-e56a-4d40-bd68-a4483b12f3bd Bertolet, A. (Alejandro)|||/items/869bcc8b-6bfc-4d1d-ba5d-0ad346750671 Terrón, J.A. (José Antonio)|||/items/2c9e1132-e349-4bc2-a919-76fe1720c114 Perales-Molina, A. (Álvaro)|||/items/8b1124b9-84dd-4c60-80f9-2597148e65f4 Jiménez, R. (Rubén)|||/items/a903f655-a81e-430d-8e56-46e9917b5b2d Miras, H. (Héctor)|||/items/c071a515-a18a-4ba0-9bac-b5846f73dbad |
| author |
Macías, J. (José)|||/items/b57af471-bd60-4847-aae4-784788358314 |
| author_facet |
Macías, J. (José)|||/items/b57af471-bd60-4847-aae4-784788358314 Ortiz, A. (Antonio)|||/items/7e712154-e56a-4d40-bd68-a4483b12f3bd Bertolet, A. (Alejandro)|||/items/869bcc8b-6bfc-4d1d-ba5d-0ad346750671 Terrón, J.A. (José Antonio)|||/items/2c9e1132-e349-4bc2-a919-76fe1720c114 Perales-Molina, A. (Álvaro)|||/items/8b1124b9-84dd-4c60-80f9-2597148e65f4 Jiménez, R. (Rubén)|||/items/a903f655-a81e-430d-8e56-46e9917b5b2d Miras, H. (Héctor)|||/items/c071a515-a18a-4ba0-9bac-b5846f73dbad |
| author_role |
author |
| author2 |
Ortiz, A. (Antonio)|||/items/7e712154-e56a-4d40-bd68-a4483b12f3bd Bertolet, A. (Alejandro)|||/items/869bcc8b-6bfc-4d1d-ba5d-0ad346750671 Terrón, J.A. (José Antonio)|||/items/2c9e1132-e349-4bc2-a919-76fe1720c114 Perales-Molina, A. (Álvaro)|||/items/8b1124b9-84dd-4c60-80f9-2597148e65f4 Jiménez, R. (Rubén)|||/items/a903f655-a81e-430d-8e56-46e9917b5b2d Miras, H. (Héctor)|||/items/c071a515-a18a-4ba0-9bac-b5846f73dbad |
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author author author author author author |
| dc.contributor.none.fl_str_mv |
Dadun. Depósito Académico Digital Universidad de Navarra |
| dc.subject.none.fl_str_mv |
Cloud computing Monte Carlo Radiotherapy |
| topic |
Cloud computing Monte Carlo Radiotherapy |
| description |
Background A 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. Methods 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. Results 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%. Conclusions 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. |
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2018 |
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2018 2018-01-01 2018 2018-01-01 2019 2019-06-04 |
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journal article http://purl.org/coar/resource_type/c_6501 |
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info:eu-repo/semantics/article |
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article |
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https://hdl.handle.net/10171/57273 |
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Inglés eng |
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Inglés |
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open access http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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