A fully parallel algorithm for multimodal image registration using normalized gradient fields

We present a super fast variational algorithm for the challenging problem of multimodal image registration. It is capable of registering full-body CT and PET images in about a second on a standard CPU with virtually no memory requirements. The algorithm is founded on a Gauss-Newton optimization scheme with specifically tailored, mathematically optimized computations for objective function and derivatives. It is fully parallelized and perfectly scalable, thus directly suitable for usage in many-core environments. The accuracy of our method was tested on 21 PET-CT scan pairs from clinical routine. The method was able to correct random distortions in the range from -10 cm to 10 cm translation and from -15° to 15° degree rotation to subvoxel accuracy. In addition, it exhibits excellent robustness to noise.