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

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 block sequ...

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Detalles Bibliográficos
Autores principales: Schwyzer, Moritz, Skawran, Stephan, Gennari, Antonio G., Waelti, Stephan L., Walter, Joan Elias, Curioni-Fontecedro, Alessandra, Hofbauer, Marlena, Maurer, Alexander, Huellner, Martin W., Messerli, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344880/
https://www.ncbi.nlm.nih.gov/pubmed/37443158
http://dx.doi.org/10.1038/s41598-023-37182-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344880/
https://www.ncbi.nlm.nih.gov/pubmed/37443158
http://dx.doi.org/10.1038/s41598-023-37182-1

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