Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations

© Springer International Publishing AG 2016. Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious “curse of dimensionality” coupled with a small sample size. In this paper,we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors,we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA).NIH (NIBIB-NAC-P41EB015902, NINDS-R01NS086905, NICHD-U01HD087211