Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance
1
Zitationen
8
Autoren
2026
Jahr
Abstract
Deep learning can create full-dose PET images with less radiation exposure. Neurological applications dominate synthetic PET research, maintaining essential diagnostic detail. Challenges include limited datasets and variability in tracer uptake, necessitating further advancements.
Ähnliche Arbeiten
New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
2008 · 29.148 Zit.
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
2006 · 15.725 Zit.
Image processing with ImageJ
2004 · 11.903 Zit.
Fast robust automated brain extraction
2002 · 10.776 Zit.
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
2002 · 10.610 Zit.