Registration using sparse free-form deformations

Abstract

Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.