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Spatio-temporal Regularization for Longitudinal Registration to an Unbiased 3D Individual Template

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  • Nicolas Guizard
  • ,
  • Vladimir S. Fonov
  • ,
  • Daniel García-Lorenzo
  • ,
  • Bérengère Aubert-Broche
  • ,
  • Simon Fristed Eskildsen
  • D.Louis Collins
Neurodegenerative diseases such as Alzheimer’s disease present subtle anatomical brain changes before the appearance of clinical symptoms. Large longitudinal brain imaging datasets are now accessible to investigate these structural changes over time. However, manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each visit is analysed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for longitudinal inconsistency in the context of structure segmentation. The major contribution of this article is the individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power for detecting significant changes occurring between populations.
Original languageEnglish
Title of host publicationSpatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
EditorsStanley Durrleman, Tom Fletcher, Guido Gerig, Marc Niethammer
Number of pages12
Publication year2012
ISBN (print)978-3-642-33554-9
Publication statusPublished - 2012

    Research areas

  • Longitudinal registration, spatio-temporal consistency, unbiased template creation

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