Cranial Meninges Reconstruction Based on Convolutional Networks and Deformable Models: Applications to Longitudinal Study of Normal Aging

Peiyu Duan, Shuo Han, Lianrui Zuo, Yang An, Yihao Liu, Ahmed Alshareef, Junghoon Lee, Aaron Carass, Susan M. Resnick, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The cranial meninges are membranes enveloping the brain. The space between these membranes contains mainly cerebrospinal fluid. 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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Ivana Isgum, Bennett A. Landman, Murray H. Loew
PublisherSPIE
ISBN (Electronic)9781510649392
DOIs
StatePublished - 2022
EventMedical Imaging 2022: Image Processing - Virtual, Online
Duration: Mar 21 2021Mar 27 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12032
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Image Processing
CityVirtual, Online
Period3/21/213/27/21

Keywords

  • MRI
  • Meninges
  • convolutional networks
  • deformable models
  • harmonization
  • longitudinal study

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging
  • Biomaterials

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