V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal
  Correspondence
Fabian Bongratz Jan Fecht Anne-Marie Rickmann Christian Wachinger
Reconstructing the cortex from longitudinal MRI is indispensable for
analyzing morphological changes in the human brain. Despite the recent
disruption of cortical surface reconstruction with deep learning, challenges
arising from longitudinal data are still persistent. Especially the lack of
strong spatiotemporal point correspondence hinders downstream analyses due to
the introduced noise. To address this issue, we present V2C-Long, the first
dedicated deep learning-based cortex reconstruction method for longitudinal
MRI. In contrast to existing methods, V2C-Long surfaces are directly comparable
in a cross-sectional and longitudinal manner. We establish strong inherent
spatiotemporal correspondences via a novel composition of two deep mesh
deformation networks and fast aggregation of feature-enhanced within-subject
templates. The results on internal and external test data demonstrate that
V2C-Long yields cortical surfaces with improved accuracy and consistency
compared to previous methods. Finally, this improvement manifests in higher
sensitivity to regional cortical atrophy in Alzheimer's disease.