Deep residual inception encoder‐decoder network for amyloid PET harmonization - 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; et al. (Alzheimer's & dementia, 2022)
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.