Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients

BACKGROUND: Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncolog...

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Detalles Bibliográficos
Autores principales: Olin, Anders B., Hansen, Adam E., Rasmussen, Jacob H., Jakoby, Björn, Berthelsen, Anne K., Ladefoged, Claes N., Kjær, Andreas, Fischer, Barbara M., Andersen, Flemming L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927520/
https://www.ncbi.nlm.nih.gov/pubmed/35294629
http://dx.doi.org/10.1186/s40658-022-00449-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927520/
https://www.ncbi.nlm.nih.gov/pubmed/35294629
http://dx.doi.org/10.1186/s40658-022-00449-z

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