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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Vydáno v:2013 IEEE 10th International Symposium on Biomedical Imaging s. 572 - 575
Hlavní autoři: Ruhaak, J., Konig, L., Hallmann, M., Papenberg, N., Heldmann, S., Schumacher, H., Fischer, B.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2013
Témata:
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
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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.
AbstractList 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.
Author Heldmann, S.
Fischer, B.
Schumacher, H.
Papenberg, N.
Konig, L.
Ruhaak, J.
Hallmann, M.
Author_xml – sequence: 1
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  surname: Ruhaak
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  surname: Konig
  fullname: Konig, L.
  organization: Fraunhofer MEVIS Project Group Image Registration, Lubeck, Germany
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  organization: Fraunhofer MEVIS Project Group Image Registration, Lubeck, Germany
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  organization: Fraunhofer MEVIS Project Group Image Registration, Lubeck, Germany
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  organization: Fraunhofer MEVIS Project Group Image Registration, Lubeck, Germany
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  organization: MiE Med. Imaging Electron. GmbH, Seth, Germany
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  givenname: B.
  surname: Fischer
  fullname: Fischer, B.
  organization: Fraunhofer MEVIS Project Group Image Registration, Lubeck, Germany
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Snippet 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...
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SubjectTerms Accuracy
Algorithm design and analysis
Biomedical imaging
Computational efficiency
Computed tomography
Image registration
Linear programming
Parallel algorithms
Positron emission tomography
Title A fully parallel algorithm for multimodal image registration using normalized gradient fields
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