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Deep residual inception encoder‐decoder network for amyloid PET harmonization
Author(s) -
Shah Jay,
Gao Fei,
Li Baoxin,
Ghisays Valentina,
Luo Ji,
Chen Yinghua,
Lee Wendy,
Zhou Yuxiang,
Benzinger Tammie L.S.,
Reiman Eric M.,
Chen Kewei,
Su Yi,
Wu Teresa
Publication year - 2022
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.12564
Subject(s) - generalizability theory , residual , deep learning , artificial intelligence , harmonization , positron emission tomography , computer science , artificial neural network , voxel , neuroimaging , nuclear medicine , machine learning , medical physics , pattern recognition (psychology) , medicine , psychology , algorithm , neuroscience , statistics , mathematics , physics , acoustics
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.