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Deformable Ultrasound Registration without Reconstruction

  • Rupert Brooks
  • D. Louis Collins
  • Xavier Morandi
  • Tal Arbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

Ultrasound (US) imaging is often proposed as an interoperative imaging modality. This use nearly always requires that the collected data be registered to preoperative data of another modality. Existing intensity-based registration approaches all begin by reconstructing a 3D US volume from the collected 2D slices. We propose to directly register the set of 2D slices to the preoperative images. We argue this has a number of advantages, including the omission of the potentially complex reconstruction step, greater adaptability of the similarity measures, and easier parallelization. We describe a system for performing this task and present results on phantom data that show that our slice based method consistently outperforms a reconstruction based method in both speed and accuracy.

Keywords

Mutual Information Image Registration Rigid Registration Registration Approach Volume Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Solberg, O.V., et al.: Freehand 3d ultrasound reconstruction algorithms –a review. Ultrasound in Medicine and Biology 33(7), 991–1009 (2007)CrossRefGoogle Scholar
  2. 2.
    Coupé, P.: Méthode de compensation des déformations cérébrales par imagerie ultrasonore intraopératoire pour la neurochirurgie guidée par l’image. PhD thesis, Université de Rennes (January 2008)Google Scholar
  3. 3.
    Modersitzki, J.: Numerical Methods for Image Registration. Numerical Mathematics and Scientific Computation. Oxford University Press, Oxford (2004)zbMATHGoogle Scholar
  4. 4.
    Arbel, T., et al.: 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
  5. 5.
    Roche, A., et al.: Rigid registration of 3D US with MR images: a new approach combining intensity and gradient information. IEEE Tr. Med. Imag. 20(10), 1038–1049Google Scholar
  6. 6.
    Blackall, J.M., et al.: An image registration approach to automated calibration for freehand 3d ultrasound. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 462–471. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Letteboer, M.M.J., et al.: Brain shift estimation in image-guided neurosurgery using 3d ultrasound. IEEE Transactions on Biomedical Engineering 52(2) (2004)Google Scholar
  8. 8.
    Sanches, J.M., Marques, J.S.: Joint image registration and volume reconstruction for 3d ultrasound. Pattern Recognition Letters 24, 791–800 (2003)CrossRefGoogle Scholar
  9. 9.
    Park, H., Meyer, C., Kim, B.: Improved motion correction in fMRI by joint mapping of slices into an anatomical volume. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 745–751. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Penney, G.P., et al.: Registration of freehand 3d ultrasound and magnetic resonance liver images. Medical Image Analysis 8(1), 81–91 (2004)CrossRefGoogle Scholar
  11. 11.
    Mattes, D., et al.: PET-CT image registration in the chest using free-form deformations. IEEE Trans. Medical Imaging 22(1), 120–128 (2003)CrossRefGoogle Scholar
  12. 12.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence 12(7) (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rupert Brooks
    • 1
  • D. Louis Collins
    • 2
  • Xavier Morandi
    • 3
  • Tal Arbel
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityCanada
  2. 2.Dept. of Biomedical EngineeringMcGill UniversityCanada
  3. 3.Dept. of NeurosurgeryUniversity Hospital of RennesFrance

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