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 sche...

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Bibliographic Details
Published in:2013 IEEE 10th International Symposium on Biomedical Imaging pp. 572 - 575
Main Authors: Ruhaak, J., Konig, L., Hallmann, M., Papenberg, N., Heldmann, S., Schumacher, H., Fischer, B.
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2013
Subjects:
Accuracy
Algorithm design and analysis
Biomedical imaging
Computational efficiency
Computed tomography
Image registration
Linear programming
Parallel algorithms
Positron emission tomography
ISBN:1467364568, 9781467364560
ISSN:1945-7928
Online Access:Get full text
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Summary: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.
ISBN:1467364568
9781467364560
ISSN:1945-7928
DOI:10.1109/ISBI.2013.6556539