Impact of MRI technology on Alzheimer's disease detection

Alzheimer's disease (AD) can be detected using magnetic resonance imaging (MRI) based features and supervised classifiers. The subcortical and ventricular volumes change for AD patients. These volumes can be extracted from MRI by tools such as Free Surfer and multi-atlas-based likelihood fusion (MALF) algorithm. Medical imaging centers typically use MRI protocols for brain scanning.These protocol differences include different scanner models with various operating parameters. The scanner models can have the same or different field strengths. A key factor in classifying multicentric MR subject images having different protocols is how different scanner models affect the extraction of features, and subsequent classification performance of a supervised classifier. We have investigated the classification performance of FreeSurfer and MALF based volume features together with Radial Basis Function Support Vector Machine and Extreme Learning Machine across different imaging protocols. We have also investigated both FreeSurfer and MALF, whose defined regions of the brain are most effective for the detection of the disease over different protocols. Our study result indicates marginal differences in classification performance across scanner models with the same or different field strengths when differentiating AD, Mild Cognitive Impairment, and Normal Controls.We have also observed differences in ranking order of the most effective regions.