Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

PURPOSE: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly r...

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Detalles Bibliográficos
Autores principales: Shiri, Isaac, Vafaei Sadr, Alireza, Akhavan, Azadeh, Salimi, Yazdan, Sanaat, Amirhossein, Amini, Mehdi, Razeghi, Behrooz, Saberi, Abdollah, Arabi, Hossein, Ferdowsi, Sohrab, Voloshynovskiy, Slava, Gündüz, Deniz, Rahmim, Arman, Zaidi, Habib
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742659/
https://www.ncbi.nlm.nih.gov/pubmed/36508026
http://dx.doi.org/10.1007/s00259-022-06053-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742659/
https://www.ncbi.nlm.nih.gov/pubmed/36508026
http://dx.doi.org/10.1007/s00259-022-06053-8

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