Development and validation of 3D super-resolution convolutional neural network for 18F-FDG-PET images

Development and validation of 3D super-resolution convolutional neural network for 18F-FDG-PET images

Abstract Background Positron emission tomography (PET) is a valuable tool for cancer diagnosis but generally has a lower spatial resolution compared to computed tomography (CT) or magnetic resonance imaging (MRI). High-resolution PET scanners that use silicon photomultipliers and time-of-flight meas...

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Bibliographic Details
Main Authors: Hiroki Endo, Kenji Hirata, Keiichi Magota, Takaaki Yoshimura, Chietsugu Katoh, Kohsuke Kudo
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:EJNMMI Physics
Subjects:
PET
Super-resolution
Deep learning
Convolutional neural network
SiPM-PET/CT
Online Access:https://doi.org/10.1186/s40658-025-00791-y
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https://doi.org/10.1186/s40658-025-00791-y

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