Deep residual inception encoder-decoder network for amyloid PET harmonization.

ABSTRACT:

Introduction

Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy.

Method

A Residual Inception Encoder-Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound-B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10-fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects.

Results

Significantly stronger between-tracer correlations (P < .001) were observed after harmonization for both global amyloid burden

SUBMITTER: Shah J 

PROVIDER: S-EPMC9360199 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature