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
