Deep residual inception encoder-decoder network for amyloid PET harmonization

Jay Shah, Fei Gao, Baoxin Li, Valentina Ghisays, Ji Luo, Yinghua Chen, Wendy Lee, Yuxiang Zhou, Tammie L.S. Benzinger, Eric M. Reiman, Kewei Chen, Yi Su, Teresa Wu

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
JournalAlzheimer's and Dementia
DOIs
StateAccepted/In press - 2022

Keywords

  • Alzheimer's disease
  • Centiloid
  • amyloid PET

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

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