Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance

Abstract Background Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represents a promising approach to minimize radiation exposure while preserving diagnostic accuracy. However, variability in methodologies, performance metrics, and clinical applications needs to be a...

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Detalhes bibliográficos

Principais autores:	Maria Vaccaro, Enrico Rosa, Elisa Placidi, Alessia Guarnera, Aurelio Secinaro, Carlo Gandolfo, Maria Carmen Garganese, Antonio Napolitano
Formato:	Artigo
Idioma:	Inglês
Publicado em:	SpringerOpen 2026-02-01
Colecção:	European Radiology Experimental
Assuntos:	Artificial intelligence Deep learning Magnetic resonance imaging Positron emission tomography Tomography (x-ray computed)
Acesso em linha:	https://doi.org/10.1186/s41747-025-00651-5
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