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...

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Bibliografiske detaljer

Principais autores:	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
Format:	Artigo
Sprog:	Inglês
Udgivet:	SpringerOpen 2026-05-01
Serier:	EJNMMI Physics
Fag:	Pediatric PET imaging Dose reduction Patch-based deep learning
Online adgang:	https://doi.org/10.1186/s40658-026-00876-2
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