Abstract
A brain image registration algorithm, referred to as RABBIT, is proposed to achieve fast and accurate image registration with the help of an intermediate template generated by a statistical deformation model. The statistical deformation model is built by principal component analysis (PCA) on a set of training samples of brain deformation fields that warp a selected template image to the individual brain samples. The statistical deformation model is capable of characterizing individual brain deformations by a small number of parameters, which is used to rapidly estimate the brain deformation between the template and a new individual brain image. The estimated deformation is then used to warp the template, thus generating an intermediate template close to the individual brain image. Finally, the shape difference between the intermediate template and the individual brain is estimated by an image registration algorithm, e.g., HAMMER. The overall registration between the template and the individual brain image can be achieved by directly combining the deformation fields that warp the template to the intermediate template, and the intermediate template to the individual brain image. The algorithm has been validated for spatial normalization of both simulated and real magnetic resonance imaging (MRI) brain images. Compared with HAMMER, the experimental results demonstrate that the proposed algorithm can achieve over five times speedup, with similar registration accuracy and statistical power in detecting brain atrophy.