A 3D Statistical Fluid Registration Algorithm

In this paper, we further investigate a Statistically-Assisted Fluid Image Registration Algorithm (SAFIRA) that was recently developed in [2]. SAFIRA was built in a Lagrangian framework, which naturally incorporates two types of statistics on shape variations deformation vectors and tensors in the regularization term of the registration. This makes structural brain MRI registrations more accurate and biologically realistic. Here, we add to the understanding of the energetic behavior of the system through looking at its Hamiltonian. Furthermore, we compare the vector-statistics version to the non-statistical one and to the widely-used fluid registration, which is based on the Navier-Stokes equation. For the statistical registration, we use prior information on a training set independent from the dataset to be analyzed. Registration accuracy is measured on a pre-labeled data set for all 3 methods, and we also compute the heritability of brain structure using 46 twin pairs. SAFIRA detected genetic effects more extensively and showed improved registration accuracy compared to the other two algorithms.