Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy

PURPOSE: Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural netwo...

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Detalles Bibliográficos
Autores principales: Sivabhaskar, Sruthi, Li, Ruiqi, Roy, Arkajyoti, Kirby, Neil, Fakhreddine, Mohamad, Papanikolaou, Nikos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Radiation Oncology Physics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359011/
https://www.ncbi.nlm.nih.gov/pubmed/35670318
http://dx.doi.org/10.1002/acm2.13667

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359011/
https://www.ncbi.nlm.nih.gov/pubmed/35670318
http://dx.doi.org/10.1002/acm2.13667

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