RESEARCH ARTICLE Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template

Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analy-sis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scan-ners 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 potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with differ-ent sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regulari-zation yields more consistent rates of change of global structures resulting in better statisti-cal power to detect significant changes over time and between populations