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

Jay Shah^1,2, Fei Gao^1,2, Baoxin Li^1,2

¹ASU-Mayo Center for Innovative Imaging, Arizona State University, 699 S. Mill Ave., Tempe, Arizona, 85287, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association

|February 10, 2022

Summary

This summary is machine-generated.

A deep learning model harmonizes amyloid positron emission tomography (PET) imaging from different tracers, improving data consistency for analysis. This strategy enhances between-tracer correlations for accurate amyloid burden assessment.

Keywords:

Alzheimer's disease Centiloid amyloid PET

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Area of Science:

Neuroimaging
Artificial Intelligence

Background:

Multiple positron emission tomography (PET) tracers exist for amyloid imaging.
This variety presents challenges for consistent interpretation and quantitative analysis.

Purpose of the Study:

To develop and validate a deep learning model for harmonizing amyloid PET imaging data acquired with different tracers.
To improve the reliability of quantitative analysis and interpretation of amyloid PET scans.

Main Methods:

A Residual Inception Encoder-Decoder Neural Network was developed.
The model was trained on 92 subjects and validated on an independent dataset of 46 subjects.

Main Results:

Harmonization significantly improved between-tracer correlations for global amyloid burden and voxel-wise measurements (P < .001).
These improvements were observed in both the training and external testing cohorts.

Conclusions:

A novel deep learning model effectively harmonizes amyloid PET imaging data across different tracers.
Further research is underway to refine the model and extend its application to additional tracers.