Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network

Abstract

In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain's basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates the EPVS rating directly from the T2-weighted MRI without using either the detection or the segmentation of EVPS. The model uses the cropped basal ganglia region on the T2-weighted MRI. We formulated the rating of EPVS as a multi-class classification problem. Model performance was evaluated using 96 subjects' T2-weighted MRI data that were collected from two hospitals. The results show that the proposed method can automatically rate EPVS-demonstrating great potential to be used as a risk indicator of dementia to aid early diagnosis.