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

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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Bibliographic Details
Main Authors: Fang Liu, Hyungseok Jang, Richard Kijowski, Gengyan Zhao, Tyler Bradshaw, Alan B. McMillan
Format: Article
Language:English
Published: SpringerOpen 2018-11-01
Series:EJNMMI Physics
Subjects:
Deep learning
PET
CT
MRI
PET/MR
PET/CT
Online Access:http://link.springer.com/article/10.1186/s40658-018-0225-8

Internet

http://link.springer.com/article/10.1186/s40658-018-0225-8

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