Impact of MRI Protocols on Alzheimer's Disease Detection

Abstract

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 FreeSurfer and the multi-atlas-based likelihood fusion (MALF) algorithm. Studies use MRI from many medical imaging centers. However, individual centers typically use distinctive MRI protocols for brain scanning. The protocol differences include different scanner models with various operating parameters. Some scanner models have different field strengths. A key factor in classifying multicentric MR subject images having different protocols is how different scanner models affect the extraction of feature, and the 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 for both FreeSurfer and MALF, which brain regions are most effective for the detection of the disease under 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 brain regions.