Probabilistic brain atlas construction: Thin-plate splinewarping via maximization of mutual information

Abstract

The concepts of a probabilistic atlas are well known. The dispersions of the resulting atlas’ spatial probability distributions depend not only on the intrinsic variation of structures between subjects, but also on the ability of the intersubject mapping method to compensate for gross spatial variations. We demonstrate an automatic method of registering patients to an atlas by maximization the mutual information between the atlas and the patient’s gray scale data set. The global thin-plate spline (TPS) transformation for mapping each subject is computed by automatically optimizing the loci of 40 control points distributed within the atlas. The use of 40 control points, i.e. 3*40=120 degrees of freedom (DOF), is a compromise between viscous flow methods with huge DOF, and the 12 DOF affine mapping. We quantitatively compare the results between using a full affine transformation versus the MIdriven 40 control point thin-plate spline for the mean and standard deviation volume data sets computed over the gray scale volumes of 7 patients.