Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition

Abstract Background PET/CT image quality is directly influenced by the F-18-FDG injected activity. The higher the injected activity, the less noise in the reconstructed images but the more radioactive staff exposition. A new FDA cleared software has been introduced to obtain clinical PET images, acq...

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Bibliografiset tiedot
Päätekijät: Gerald Bonardel, Axel Dupont, Pierre Decazes, Mathieu Queneau, Romain Modzelewski, Jeremy Coulot, Nicolas Le Calvez, Sébastien Hapdey
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: SpringerOpen 2022-05-01
Sarja:EJNMMI Physics
Aiheet:
PET/CT
Deep-learning
Denoising
Linkit:https://doi.org/10.1186/s40658-022-00465-z
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