External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data

External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data

Abstract Background A reduction of dose and/or acquisition duration of PET examinations is desirable in terms of radiation protection, patient comfort and throughput, but leads to decreased image quality due to poorer image statistics. Recently, different deep-learning based methods have been propos...

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Bibliographic Details
Main Authors: Anja Braune, René Hosch, David Kersting, Juliane Müller, Frank Hofheinz, Ken Herrmann, Felix Nensa, Jörg Kotzerke, Robert Seifert
Format: Article
Language:English
Published: SpringerOpen 2025-04-01
Series:EJNMMI Physics
Subjects:
Deep-learning
Denoising
PET image quality
Phantom-based validation
Online Access:https://doi.org/10.1186/s40658-025-00745-4
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