A new multi-modal similarity measure for fast gradient-based 2D-3D image registration

2D-3D image registration has been adopted in many clinical applications such as image-guided surgery and the kinematic analysis of bones in knee and ankle joints. In this paper we propose a new single-plane 2D-3D registration algorithm which requires far less iteration than previous techniques. The new algorithm includes a new multi-modal similarity measure and a novel technique for the analytic calculation of the required gradients. Our experimental results show that, when compared to existing gradient and non-gradient based techniques, the proposed algorithm has a wider range of initial poses for which registration can be achieved and requires significantly fewer iterations to converge to the true 3D position of the anatomical structure.