Deep residual inception encoder‐decoder network for amyloid PET harmonization

• Published in: Alzheimer's & dementia Vol. 18; no. 12; pp. 2448 - 2457
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States John Wiley and Sons Inc 01.12.2022
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Amyloid - metabolism; amyloid PET; Amyloidogenic Proteins; Aniline Compounds; Brain - diagnostic imaging; Brain - metabolism; Centiloid; Humans; Positron-Emission Tomography - methods; Radiopharmaceuticals; Alzheimer's disease; Centiloid; amyloid PET
• ISSN: 1552-5260; 1552-5279; 1552-5279
• DOI: 10.1002/alz.12564

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.