A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer

Background and purpose Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasi...

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Detalles Bibliográficos
Autores principales: Maspero, Matteo, Houweling, Antonetta C., Savenije, Mark H.F., van Heijst, Tristan C.F., Verhoeff, Joost J.C., Kotte, Alexis N.T.J., van den Berg, Cornelis A.T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807541/
https://www.ncbi.nlm.nih.gov/pubmed/33458310
http://dx.doi.org/10.1016/j.phro.2020.04.002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807541/
https://www.ncbi.nlm.nih.gov/pubmed/33458310
http://dx.doi.org/10.1016/j.phro.2020.04.002

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