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

Abstract 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 continu...

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Autors principals:	Fang Liu, Hyungseok Jang, Richard Kijowski, Gengyan Zhao, Tyler Bradshaw, Alan B. McMillan
Format:	Artigo
Idioma:	Inglês
Publicat:	SpringerOpen 2018-11-01
Col·lecció:	EJNMMI Physics
Matèries:	Deep learning PET CT MRI PET/MR PET/CT
Accés en línia:	http://link.springer.com/article/10.1186/s40658-018-0225-8
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