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Automation of Monte Carlo‐based treatment plan verification for proton therapy

PURPOSE: Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in‐depth knowledge of the computational envi...

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Autores principales: Kaluarachchi, Maduka, Moskvin, Vadim, Pirlepesov, Fakhriddin, Wilson, Lydia J., Xie, Fang, Faught, Austin M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484839/
https://www.ncbi.nlm.nih.gov/pubmed/32452657
http://dx.doi.org/10.1002/acm2.12923
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author Kaluarachchi, Maduka
Moskvin, Vadim
Pirlepesov, Fakhriddin
Wilson, Lydia J.
Xie, Fang
Faught, Austin M.
author_facet Kaluarachchi, Maduka
Moskvin, Vadim
Pirlepesov, Fakhriddin
Wilson, Lydia J.
Xie, Fang
Faught, Austin M.
author_sort Kaluarachchi, Maduka
collection PubMed
description PURPOSE: Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in‐depth knowledge of the computational environment. We developed an automated framework to remove these barriers and integrate our MC model into the clinical workflow. MATERIALS AND METHODS: The Eclipse Scripting Application Programming Interface was used to initiate the automation process. A series of MATLAB scripts were then used for preprocessing of input data and postprocessing of results. Additional scripts were used to monitor the calculation process and appropriately deploy calculations to an institutional high‐performance computing facility. The automated framework and beamline models were validated against 160 patient specific QA measurements from an ionization chamber array and using a ±3%/3 mm gamma criteria. RESULTS: The automation reduced the human‐hours required to initiate and run a calculation to 1–2 min without leaving the treatment planning system environment. Validation comparisons had an average passing rate of 99.4% and were performed at depths ranging from 1 to 15 cm. CONCLUSION: An automated framework for running MC calculations was developed which enables the calculation of dose and linear energy transfer within a clinically relevant workflow and timeline. The models and framework were validated against patient specific QA measurements and exhibited excellent agreement. Before this implementation, execution was prohibitively complex for an untrained individual and its use restricted to a research environment.
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spelling pubmed-74848392020-09-17 Automation of Monte Carlo‐based treatment plan verification for proton therapy Kaluarachchi, Maduka Moskvin, Vadim Pirlepesov, Fakhriddin Wilson, Lydia J. Xie, Fang Faught, Austin M. J Appl Clin Med Phys Radiation Oncology Physics PURPOSE: Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in‐depth knowledge of the computational environment. We developed an automated framework to remove these barriers and integrate our MC model into the clinical workflow. MATERIALS AND METHODS: The Eclipse Scripting Application Programming Interface was used to initiate the automation process. A series of MATLAB scripts were then used for preprocessing of input data and postprocessing of results. Additional scripts were used to monitor the calculation process and appropriately deploy calculations to an institutional high‐performance computing facility. The automated framework and beamline models were validated against 160 patient specific QA measurements from an ionization chamber array and using a ±3%/3 mm gamma criteria. RESULTS: The automation reduced the human‐hours required to initiate and run a calculation to 1–2 min without leaving the treatment planning system environment. Validation comparisons had an average passing rate of 99.4% and were performed at depths ranging from 1 to 15 cm. CONCLUSION: An automated framework for running MC calculations was developed which enables the calculation of dose and linear energy transfer within a clinically relevant workflow and timeline. The models and framework were validated against patient specific QA measurements and exhibited excellent agreement. Before this implementation, execution was prohibitively complex for an untrained individual and its use restricted to a research environment. John Wiley and Sons Inc. 2020-05-26 /pmc/articles/PMC7484839/ /pubmed/32452657 http://dx.doi.org/10.1002/acm2.12923 Text en © 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Kaluarachchi, Maduka
Moskvin, Vadim
Pirlepesov, Fakhriddin
Wilson, Lydia J.
Xie, Fang
Faught, Austin M.
Automation of Monte Carlo‐based treatment plan verification for proton therapy
title Automation of Monte Carlo‐based treatment plan verification for proton therapy
title_full Automation of Monte Carlo‐based treatment plan verification for proton therapy
title_fullStr Automation of Monte Carlo‐based treatment plan verification for proton therapy
title_full_unstemmed Automation of Monte Carlo‐based treatment plan verification for proton therapy
title_short Automation of Monte Carlo‐based treatment plan verification for proton therapy
title_sort automation of monte carlo‐based treatment plan verification for proton therapy
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484839/
https://www.ncbi.nlm.nih.gov/pubmed/32452657
http://dx.doi.org/10.1002/acm2.12923
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