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.
Original language | English (US) |
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Pages (from-to) | 2448-2457 |
Number of pages | 10 |
Journal | Alzheimer's and Dementia |
Volume | 18 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2022 |
Keywords
- Alzheimer's disease
- Centiloid
- amyloid PET
ASJC Scopus subject areas
- Epidemiology
- Health Policy
- Developmental Neuroscience
- Clinical Neurology
- Geriatrics and Gerontology
- Cellular and Molecular Neuroscience
- Psychiatry and Mental health