This paper proposes a brain image registration algorithm, called RABBIT, which achieves fast and accurate image registration by using an intermediate template generated by a statistical shape deformation model during the image registration procedure. The statistical brain shape deformation information is learned by means of principal component analysis (PCA) from a set of training brain deformations, each of them linking a selected template to an individual brain sample. Using the statistical deformation information, the template image can be registered to a new individual image by optimizing a statistical deformation model with a small number of parameters, thus generating an intermediate template very close to the individual brain image. The remaining shape difference between the intermediate template and the individual brain is then minimized by a general registration algorithm, such as HAMMER. With the help of the intermediate template, the registration between the template and individual brain images can be achieved fast and with similar registration accuracy as HAMMER. The effectiveness of the RABBIT has been evaluated by using both simulated atrophy data and real brain images. The experimental results show that RABBIT can achieve over five times speedup, compared to HAMMER, without losing any registration accuracy or statistical power in detecting brain atrophy.