Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization
share
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 it to additional tracers.
READ FULL TEXT