Generation of synthetic PET images of synaptic density and amyloid from ¹⁸ F-FDG images using deep learning.

ABSTRACT:

Purpose

Positron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to PET scans using new or less-available tracers with sophisticated synthesis and short half-life isotopes may be very limited. Therefore, it is of great significance and interest in AD research to assess the feasibility of generating synthetic PET images of less-available tracers from the PET image of another common tracer, in particular ¹⁸ F-FDG.

Methods

We implemented advanced deep learning methods using the U-Net model to predict ¹¹ C-UCB-J PET images of synaptic vesicle protein 2A (SV2A), a surrogate of synaptic density, from ¹⁸ F-FDG PET data. Dynamic ¹⁸ F-FDG and ¹¹ C-UC

SUBMITTER: Wang R 

PROVIDER: S-EPMC8455448 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature