Large deformation inverse consistent elastic image registration.

This paper presents a new image registration algorithm that accommodates locally large nonlinear deformations. The algorithm concurrently estimates the forward and reverse transformations between a pair of images while minimizing the inverse consistency error between the transformations. It assumes that the two images to be registered contain topologically similar objects and were collected using the same imaging modality. The large deformation transformation from one image to the other is accommodated by concatenating a sequence of small deformation transformations. Each incremental transformation is regularized using a linear elastic continuum mechanical model. Results of ten 2D and twelve 3D MR image registration experiments are presented that tested the algorithm's performance on real brain shapes. For these experiments, the inverse consistency error was reduced on average by 50 times in 2D and 30 times in 3D compared to the viscous fluid registration algorithm.