Spatio-temporal regularization for longitudinal registration to an unbiased 3D individual template

Nicolas Guizard (1) , Vladimir Fonov (1) , Daniel García-Lorenzo (1, 2) , Bérengère Aubert-Broche (1) , Simon Eskildsen (3, 1) , D. Louis Collins (1)

1 MNI - McConnell Brain Imaging Centre
2 CENIR - Plateforme de neuroimagerie CENIR = Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris]
3 Aarhus University [Aarhus]

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.

Mots clés: Longitudinal registration; spatio-temporal consistency; unbiased template creation

Domaines: Imagerie

Nicolas Guizard, Vladimir Fonov, Daniel García-Lorenzo, Bérengère Aubert-Broche, Simon Eskildsen, et al.. Spatio-temporal regularization for longitudinal registration to an unbiased 3D individual template. Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, Oct 2012, France. pp.1-12. ⟨hal-00740718⟩