A deep learning approach for (18)F-FDG PET attenuation correction

BACKGROUND: To develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was developed utilizing deep learning to generate continuously va...

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Detalles Bibliográficos
Autores principales: Liu, Fang, Jang, Hyungseok, Kijowski, Richard, Zhao, Gengyan, Bradshaw, Tyler, McMillan, Alan B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230542/
https://www.ncbi.nlm.nih.gov/pubmed/30417316
http://dx.doi.org/10.1186/s40658-018-0225-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230542/
https://www.ncbi.nlm.nih.gov/pubmed/30417316
http://dx.doi.org/10.1186/s40658-018-0225-8

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