Fast nonrigid image registration using statistical deformation models learned from richly-annotated data

Nonrigid image registrations require a large number of degrees of freedom (DoFs) to capture intersubject anatomical variations. With such high DoFs and lack of anatomical correspondences, algorithms may not converge to the globally optimal solution. In this work, we propose a fast, two-step nonrigid registration procedure with low DoFs to accurately register brain images. Our method makes use of a statistical deformation model based upon a principal component analysis of deformations learned from a manually-segmented dataset to perform an initial registration. We then follow with a low DoF nonrigid transformation to complete the registration. Our results show the same registration accuracy in terms of volume of interest overlap as high DoF transformations, but with a 96% reduction in DoF and 98% decrease in computation time.