Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning.

ABSTRACT:

Purpose

Synaptic vesicle glycoprotein 2 A (SV2A) in human brains is an important biomarker of synaptic loss associated with several neurological disorders. However, SV2A tracers, such as [¹¹C]UCB-J, are less available in practice due to constrains such as cost, radiation exposure and onsite cyclotron. We therefore aim to generate synthetic [¹¹C]UCB-J PET images based on MRI in this study.

Methods

We implemented a convolution-based 3D encoder-decoder to predict [¹¹C]UCB-J SV2A PET images. A total of 160 participants who underwent both MRI and [¹¹C]UCB-J PET imaging, including individuals with schizophrenia, cannabis use disorder, Alzheimer's disease, were used in this study. The model was trained on pairs of T1-weighted MRI and [

SUBMITTER: Zheng X 

PROVIDER: S-EPMC11958861 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature