Regional analysis of FDG-PET for use in the classification of Alzheimer'S Disease

We present the first use of multi-region 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 automati...

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Bibliographic Details
Main Authors: Gray, Katherine R, Wolz, Robin, Keihaninejad, Shiva, Heckemann, Rolf A, Aljabar, Paul, Hammers, Alexander, Rueckert, Daniel
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Alzheimer's disease
Classification
Dementia
Image segmentation
Magnetic resonance imaging
Mild cognitive impairment
Positron emission tomography
Support vector machines
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Summary:We present the first use of multi-region 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 (accuracy 82%), and good discrimination between MCI patients and HC (accuracy 70%). 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.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2011.5872589