Dataset Information

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 indices and voxel-wise measurements in the training cohort and the external testing cohort.

Discussion

We proposed and validated a novel encoder-decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.

SUBMITTER: Shah J

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

REPOSITORIES: biostudies-literature

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Publications

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

Shah Jay J Gao Fei F Li Baoxin B Ghisays Valentina V Luo Ji J Chen Yinghua Y Lee Wendy W Zhou Yuxiang Y Benzinger Tammie L S TLS Reiman Eric M EM Chen Kewei K Su Yi Y Wu Teresa T

Alzheimer's & dementia : the journal of the Alzheimer's Association 20220209 12

<h4>Introduction</h4>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.<h4>Method</h4>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 ...[more]

PMID: 35142053

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Dataset Information

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

Introduction

Method

Results

Discussion

Publications

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

Similar Datasets

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