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

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...

Full description

Bibliographic Details
Main Authors: Matteo Maspero, Antonetta C. Houweling, Mark H.F. Savenije, Tristan C.F. van Heijst, Joost J.C. Verhoeff, Alexis N.T.J. Kotte, Cornelis A.T. van den Berg
Format: Article
Language:English
Published: Elsevier 2020-04-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Adaptive radiotherapy
Dose calculation
Image-guided radiotherapy
CBCT
Deep learning
Artificial intelligence
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631620300129

Internet

http://www.sciencedirect.com/science/article/pii/S2405631620300129

Similar Items