A deep learning model for generating [¹⁸F]FDG PET Images from early-phase [¹⁸F]Florbetapir and [¹⁸F]Flutemetamol PET images.

ABSTRACT:

Introduction

Amyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([¹⁸F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunction and disease progression and is complementary for AD diagnosis. Dual-scan acquisitions of amyloid PET allows the possibility to use early-phase amyloid-PET as a biomarker for neurodegeneration, proven to have a good correlation to [¹⁸F]FDG PET. The aim of this study was to evaluate the added value of synthesizing the later from the former through deep learning (DL), aiming at reducing the number of PET scans, radiation dose, and discomfort to patients.

Methods

A total of 166 subjects including cognitivel

SUBMITTER: Sanaat A 

PROVIDER: S-EPMC11445334 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature