Large deformation inverse consistent elastic image registration

Large Deformation Inverse Consistent Elastic

Image Registration

Jianchun He and Gary E.Christensen

Department of Electrical and Computer Engineering

The University of Iowa,Iowa City,IA,52242

jianchun-he@ and gary-christensen@ Abstract.This paper presents a new image registration algorithm that

accommodates locally large nonlinear deformations.The algorithm con-

currently 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 ten2D and twelve

3D MR image registration experiments are presented that tested the al-

gorithm’s performance on real brain shapes.For these experiments,the

inverse consistency error was reduced on average by50times in2D and

30times in3D compared to the viscousfluid registration algorithm.

1Introduction

Magnetic resonance images(MRI)of the head demonstrate that the macroscopic shape of the brain is complex and varies widely across normal individuals.Low-dimensional,small-deformation,and linear image registration algorithms[1–7] can only determine correspondences between brain images at a coarse global level.High dimensional large deformation image registration algorithms[8–10] are needed to describe the complex shape differences between individuals at the local level.

In this paper we present a new large-deformation,inverse-consistent,elas-tic image registration(LDCEIR)algorithm.This method accommodates large nonlinear deformations by concatenating a sequence of small incremental trans-formations.Inverse consistency between the forward and reverse transformations is achieved by jointly estimating the incremental transformations while enforcing inverse consistency constraints on each incremental transformation.The trans-formation estimation is regularized using a linear differential operator that pe-nalizes second order derivatives in both the spatial and temporal dimensions. This regularization is most similar to a thin-plate spline or linear elastic regu-larization with the difference that it is applied to both the spatial and temporal dimensions instead of just the spatial dimension.