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V2C-Long: Longitudinal cortex reconstruction with spatiotemporal correspondence.

Fabian Bongratz1,2, Jan Fecht1, Anne-Marie Rickmann1

  • 1Laboratory for AI in Medical Imaging, Technical University of Munich, Munich, Germany.

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
Summary

V2C-Long, a novel deep learning method, improves longitudinal cortical surface reconstruction from MRI scans. It establishes spatiotemporal correspondence, enhancing accuracy for brain morphology analysis in studies like Alzheimer's disease.

Keywords:
cortical surfacesdeep learninglongitudinal MRIshape correspondence

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Longitudinal magnetic resonance imaging (MRI) is crucial for tracking human brain morphological changes.
  • Current deep learning cortex reconstruction methods struggle with spatiotemporal point correspondence in longitudinal data.
  • Precise anatomical matching is essential for reliable analysis of local brain surface morphology over time.

Purpose of the Study:

  • Introduce V2C-Long, the first deep learning method specifically designed for cortex reconstruction from longitudinal MRI.
  • Address the challenge of spatiotemporal point correspondence in longitudinal brain surface analysis.
  • Improve the accuracy and consistency of cortical reconstruction for neurodegenerative disease research.

Main Methods:

  • Developed V2C-Long, a deep learning-based cortex reconstruction technique for longitudinal MRI.
  • Employed a novel approach using two deep template-deformation networks and mesh-space aggregation of within-subject templates.
  • Ensured inherent spatiotemporal correspondence during the reconstruction process.

Main Results:

  • V2C-Long demonstrated substantial improvements in longitudinal consistency and accuracy compared to existing methods.
  • Validated on two large neuroimaging studies, showing enhanced surface accuracy, consistency, generalization, and reliability.
  • Provided stronger evidence for longitudinal cortical atrophy in Alzheimer's disease than FreeSurfer.

Conclusions:

  • V2C-Long offers a significant advancement in reconstructing cortical surfaces from longitudinal MRI data.
  • The method's inherent spatiotemporal correspondence reduces the need for complex post-processing steps.
  • V2C-Long enhances the sensitivity and accuracy of detecting brain changes, particularly in conditions like Alzheimer's disease.