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A fully parallel algorithm for multimodal image registration using normalized gradient fields | IEEE Conference Publication | IEEE Xplore

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


Abstract:

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 ab...Show More

Abstract:

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.
Date of Conference: 07-11 April 2013
Date Added to IEEE Xplore: 15 July 2013
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Conference Location: San Francisco, CA, USA

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