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Regional Analysis of FDG-PET for the Classification of Alzheimer's Disease

Katherine Gray, Robin Wolz, Shiva Keihaninejad, R. A. Heckemann, Paul Aljabar, A. Hammers, Daniel Rueckert

Conference or Workshop Paper
MIUA 2011
July, 2011
Abstract

We present a multi-region analysis of FDG-PET data for classification of subjects from the Alzheimer's Disease Neuroimaging Initiative. Image data were obtained from 69 healthy controls, 71 AD patients, and 147 patients with a baseline diagnosis of MCI. Anatomical segmentations were automatically generated in the native MRI-space of each subject, and the mean signal intensity per cubic millimetre in each region was extracted from the FDG-PET images. Using a support vector machine classifier, we achieve excellent discrimination between AD patients and HC (area under ROC curve 90%), as well as between MCI patients and HC (area under ROC curve 75%). Using FDG-PET, a technique which is often used clinically in the workup of dementia patients, we achieve results which are comparable with those obtained using data from research-quality MRI, or biomarkers obtained invasively from the cerebrospinal fluid.

Notes

Available in proceedings at http://www.biomedical-image-analysis.co.uk/images/stories/gray-posters2-77.pdf

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