A nonlinear projection scheme for fast rigid registration

Intensity based rigid registration algorithms commonly employed for medical image fusion are based on the iterative optimization of a pixel-by-pixel distance measure defined on the images. As medical images grow larger in size due to advanced scanner technology, evaluating such similarity measures is no longer computationally efficient. In order to overcome the inherent limitations of the standard approach we propose a new, nonlinear projection scheme that enables a very fast evaluation of the distance between two images. Current state-of-the-art projection schemes decompose a six dimensional search space into three dimensional subspaces. The proposed approach, however, yields a complete decomposition into ID subspaces. The optimization on these subspaces is highly efficient and does not require a reprojection. This scheme is therefore suitable for 2D and 3D registrations, and it is able to cope with subvolume matching problems. Furthermore, the use of modern graphics hardware allows for a highly efficient implementation. Experiments show that computation times can be reduced to less than 10 seconds with the proposed approach for 2563 sized volumes.