BRAIN MENINGES SURFACE RECONSTRUCTION: APPLICATION TO LONGITUDINAL STUDY OF NORMAL AGING

Abstract

The cranial meninges are membranes enveloping the brain. The space between two of these membranes contains cerebrospinal fluid. Changes in the meninges have been associated with many neurodegenerative diseases. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging. Additionally, we conducted a preliminary study on 5 subjects, in which we assigned region labels to the meninges—using a fast marching algorithm from cortical labels—and calculated the thickness of the meningeal layers. In the future, we hope to apply the algorithms to larger datasets to further study the regional thickness changes in the meninges