Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

Abstract To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using b...

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Bibliographic Details
Main Authors: Moritz Schwyzer, Stephan Skawran, Antonio G. Gennari, Stephan L. Waelti, Joan Elias Walter, Alessandra Curioni-Fontecedro, Marlena Hofbauer, Alexander Maurer, Martin W. Huellner, Michael Messerli
Format: Article
Language:English
Published: Nature Portfolio 2023-07-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-37182-1

Internet

https://doi.org/10.1038/s41598-023-37182-1

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