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International Journal of Biomedical Imaging
Volume 2018 (2018), Article ID 1247430, 13 pages
https://doi.org/10.1155/2018/1247430
Research Article

Classification of Alzheimer’s and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET

1Department of Computer and Software Engineering, Polytechnique Montreal, P.O. Box 6079, Downtown Station, Montreal, QC, Canada H3C 3A7
2Center for Imaging of Neurodegenerative Disease, San Francisco VA Medical Center, University of California, San Francisco, CA, USA

Correspondence should be addressed to Samuel Kadoury; ac.ltmylop@yruodak.leumas

Received 23 November 2017; Revised 2 February 2018; Accepted 12 February 2018; Published 15 March 2018

Academic Editor: Richard H. Bayford

Copyright © 2018 Seyed Hossein Nozadi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Early identification of dementia in the early or late stages of mild cognitive impairment (MCI) is crucial for a timely diagnosis and slowing down the progression of Alzheimer’s disease (AD). Positron emission tomography (PET) is considered a highly powerful diagnostic biomarker, but few approaches investigated the efficacy of focusing on localized PET-active areas for classification purposes. In this work, we propose a pipeline using learned features from semantically labelled PET images to perform group classification. A deformable multimodal PET-MRI registration method is employed to fuse an annotated MNI template to each patient-specific PET scan, generating a fully labelled volume from which 10 common regions of interest used for AD diagnosis are extracted. The method was evaluated on 660 subjects from the ADNI database, yielding a classification accuracy of 91.2% for AD versus NC when using random forests combining features from cross-sectional and follow-up exams. A considerable improvement in the early versus late MCI classification accuracy was achieved using FDG-PET compared to the AV-45 compound, yielding a 72.5% rate. The pipeline demonstrates the potential of exploiting longitudinal multiregion PET features to improve cognitive assessment.