Elastic mapping technique for intersubject tomographic image registration

This paper presents a two step self-organizing method for a transformation that can elastically map one subject's MR image, called the input image, to a standard reference MR image. Linear scaling and transformation are first introduced to grossly match the input image to the reference image. Then the input image is linearly scaled and divided into several smaller cubes of equal volume. A local correspondence is used to estimate the best matching position by moving individual cubes of the input image to the reference image within a search neighborhood. Based on local correspondence, coarse displacement vectors for each cube are determined by the position difference between the original and the new cube centers. The estimated vectors provide a complete transformation that matches the entire input image to the reference image. As the process is repeated, a better transformation is obtained that improves the matching. This algorithm has been tested on simulations of 3D deformed images and been found to be successful for 3D inter-subject registration of MR images.