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Fast and Robust Registration Based on Gradient Orientations: Case Study Matching Intra-operative Ultrasound to Pre-operative MRI in Neurosurgery

  • Dante De Nigris
  • D. Louis Collins
  • Tal Arbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7330)

Abstract

We present a novel approach for the rigid registration of pre-operative magnetic resonance to intra-operative ultrasound in the context of image-guided neurosurgery. Our framework proposes the maximization of gradient orientation alignment in locations with minimal uncertainty of the orientation estimates, permitting fast and robust performance. We evaluated our method on 14 clinical neurosurgical cases of patients with brain tumors, including low-grade and high-grade gliomas. We demonstrate processing times as small as 7 seconds and improved performance with relation to competing intensity-based methods.

Keywords

Image Registration Neurosurgery Ultrasound 

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References

  1. 1.
    Anonymous: Online database of clinical MR and ultrasound images of brain tumours. Submitted to Medical Physics (October 2011)Google Scholar
  2. 2.
    Arbel, T., Morandi, X., Comeau, R.M., Collins, D.L.: Automatic Non-linear MRI-Ultrasound Registration for the Correction of Intra-operative Brain Deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 913–922. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Brooks, R., Collins, D.L., Morandi, X., Arbel, T.: Deformable Ultrasound Registration without Reconstruction. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1023–1031. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Dai, Y., Tian, J., Dong, D., Yan, G., Zheng, H.: Real-time visualized freehand 3d ultrasound reconstruction based on gpu. IEEE Transactions on Information Technology in Biomedicine 14(6), 1338–1345 (2010)CrossRefGoogle Scholar
  5. 5.
    De Nigris, D., Mercier, L., Del Maestro, R., Louis Collins, D., Arbel, T.: Hierarchical Multimodal Image Registration Based on Adaptive Local Mutual Information. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 643–651. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Haber, E., Modersitzki, J.: Beyond mutual information: A simple and robust alternative. In: Meinzer, H.P., Handels, H., Horsch, A., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2005. Informatik aktuell. Springer (2005)Google Scholar
  7. 7.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9, 159–195 (2001)CrossRefGoogle Scholar
  8. 8.
    Ji, S., Wu, Z., Hartov, A., Roberts, D.W., Paulsen, K.D.: Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Medical Physics 35(10), 4612–4624 (2008)CrossRefGoogle Scholar
  9. 9.
    Karaçali, B.: Information theoretic deformable registration using local image information. IJCV 72, 219–237 (2007)CrossRefGoogle Scholar
  10. 10.
    Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.: elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  11. 11.
    Letteboer, M., Willems, P., Viergever, M., Niessen, W.: Brain shift estimation in image-guided neurosurgery using 3-D ultrasound. IEEE Transactions on Biomedical Engineering 52(2), 268–276 (2005)CrossRefGoogle Scholar
  12. 12.
    Mercier, L., Fonov, V., Haegelen, C., Del Maestro, R., Petrecca, K., Collins, D.: Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery. International Journal of Computer Assisted Radiology and Surgery, 1–12 (2011)Google Scholar
  13. 13.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Roche, A., Pennec, X., Malandain, G., Ayache, N.: Rigid registration of 3D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Trans. Med. Imaging 20, 1038–1049 (2001)CrossRefGoogle Scholar
  15. 15.
    Wein, W., Brunke, S., Khamene, A., Callstrom, M.R., Navab, N.: Automatic ct-ultrasound registration for diagnostic imaging and image-guided intervention. Medical Image Analysis 12(5), 577–585 (2008)CrossRefGoogle Scholar
  16. 16.
    Zhang, W., Noble, J., Brady, J.: Adaptive Non-rigid Registration of Real Time 3D Ultrasound to Cardiovascular MR Images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 50–61. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dante De Nigris
    • 1
  • D. Louis Collins
    • 2
  • Tal Arbel
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontrealCanada
  2. 2.Montreal Neurological InstituteMcGill UniversityMontrealCanada

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