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

: Rühaak, J.; König, L.; Hallmann, M.; Papenberg, N.; Heldmann, S.; Schumacher, H.; Fischer, B.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society; IEEE Engineering in Medicine and Biology Society -EMBS-:
IEEE 10th International Symposium on Biomedical Imaging, ISBI 2013. From Nano to Macro. Vol.1 : San Francisco, California, USA, 7 - 11 April 2013
New York, NY: IEEE, 2013
ISBN: 978-1-4673-6456-0 (Print)
ISBN: 978-1-4673-6454-6
ISBN: 978-1-4673-6455-3
International Symposium on Biomedical Imaging (ISBI) <10, 2013, San Francisco/Calif.>
Conference Paper
Fraunhofer MEVIS ()

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.