Longitudinal FDG-PET features for the classification of Alzheimer´s disease

The majority of existing computer-aided diagnosis (CAD) schemes for Alzheimer´s disease (AD) rely on the analysis of biomarkers at a single time-point, ignoring the progressive nature of the disorder. Recently, a method was proposed by Gray et al. [1] for the multi-region analysis of longitudinal fluorodeoxyglucose positron emission tomography (FDG-PET) images which reported classification improvements by using regional signal intensities combined with regional change over a 12 month period. In this paper we extend the approach proposed in [1] to the analysis of the entire brain pattern. Compared to [1], our method uses voxel-wise differences and avoids segmentation of the images into regions of interest. For our study, FDG-PET scans at the baseline and at 12-month follow-up of cognitively normal (CN), mild cognitive impairment (MCI) and AD subjects were retrieved from the Alzheimer´s disease neuroimaging initiative (ADNI) database. For both AD and MCI identification, the best classification results were achieved by combining cross-sectional and longitudinal information rather than using only the cross-sectional data. Furthermore, the longitudinal voxel-based analysis outperformed multi-region analysis.