Image registration using a symmetric prior - in three dimensions

This paper describes a Bayesian method for three-dimensional registration o f brain images. A finite element approach is used to obtain a maximum a pos teriori estimate of the deformation field at every voxel of a template volu me. The priors used by the MAP estimate penalize unlikely deformations and enforce a continuous one-to-one mapping. The deformations are assumed to ha ve some form of symmetry, in that priors describing the probability distrib ution of the deformations should be identical to those for the inverses (i. e., warping brain A to brain B should not be different probablistically fro m warping B to A). A gradient descent algorithm is presented lor estimating the optimum deformations. (C) 2000 Wiley-Liss, Inc.