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...

Volledige beschrijving

Bewaard in:
Bibliografische gegevens
Hoofdauteurs: Gerald Bonardel, Axel Dupont, Pierre Decazes, Mathieu Queneau, Romain Modzelewski, Jeremy Coulot, Nicolas Le Calvez, Sébastien Hapdey
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: SpringerOpen 2022-05-01
Reeks:EJNMMI Physics
Onderwerpen:
PET/CT
Deep-learning
Denoising
Online toegang:https://doi.org/10.1186/s40658-022-00465-z
Tags: Voeg label toe
Geen labels, Wees de eerste die dit record labelt!

Gelijkaardige items