Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia |
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| Authors: | Esther E Bron Rebecca ME Steketee Gavin C Houston Ruth A Oliver Hakim C Achterberg Marco Loog John C van Swieten Alexander Hammers Wiro J Niessen Marion Smits Stefan Klein for the Alzheimer's Disease Neuroimaging Initiative |
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| Affiliation: | 1. Departments of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC ‐ University Medical Center Rotterdam, the Netherlands;2. Department of Radiology, Erasmus MC ‐ University Medical Center Rotterdam, the Netherlands;3. GE Healthcare, Hoevelaken, the Netherlands;4. Brain Repair and Rehabilitation, Institute of Neurology, University College London, United Kingdom;5. Pattern Recognition Laboratory, Delft University of Technology, Delft, the Netherlands;6. Department of Neurology, Erasmus MC ‐ University Medical Center Rotterdam, the Netherlands;7. Fondation Neurodis, CERMEP‐Imagerie du Vivant, Lyon, France;8. Division of Brain Sciences, Faculty of Medicine, Imperial College London, United Kingdom;9. Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands |
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| Abstract: | Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel‐wise method and a region of interest (ROI)‐wise approach using five ROI‐sets in the GM. These ROI‐sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature‐extraction approach achieved more accurate results (area under the curve (AUC) range = 86 ? 91%) than all other approaches (AUC = 57 ? 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself. Hum Brain Mapp 35:4916–4931, 2014. © 2014 Wiley Periodicals, Inc . |
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| Keywords: | Alzheimer's disease arterial spin labeling classification diagnostic imaging frontotemporal dementia magnetic resonance imaging presenile dementia support vector machines |
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