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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...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley and Sons Inc.
2022
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| Materias: | |
| 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 |