Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning

Abstract Background Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging. However, conventional DL approaches typically require large and diverse training datasets to achieve generalizability. We propose a patch-ba...

Celý popis

Podrobná bibliografie

Hlavní autoři:	Chenyang Han, Andrew T. Trout, Andi Li, Joseph G. Meier, Vinicius P. V. Alves, Joseph MacLean, Nadeen Abu Ata, Yinan Li, Susan E. Sharp, Christopher G. Anton, Jing Tang
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	SpringerOpen 2026-05-01
Edice:	EJNMMI Physics
Témata:	Pediatric PET imaging Dose reduction Patch-based deep learning
On-line přístup:	https://doi.org/10.1186/s40658-026-00876-2
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!