Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network

Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network

Abstract Background The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images. Methods A convolutional neural network (CNN) was trained by using pairs of excellent (acqui...

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Bibliographic Details
Main Authors: John Ly, David Minarik, Jonas Jögi, Per Wollmer, Elin Trägårdh
Format: Article
Language:English
Published: SpringerOpen 2021-05-01
Series:EJNMMI Research
Subjects:
Cancer
Artificial intelligence
PET
Image quality
Online Access:https://doi.org/10.1186/s13550-021-00788-5

Internet

https://doi.org/10.1186/s13550-021-00788-5

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