Advertisement

Abstract

We describe an algorithm and its implementation details for automatic image-based registration of intra-operative ultrasound to MRI for brain-shift correction during neurosurgery. It is evaluated on a public database of 22 surgeries for retrospective evaluation, with a particular focus on choosing the appropriate transformation model and designing the most meaningful evaluation strategy. The method succeeds in a fully automatic fashion in all cases, with an average landmark registration error for the rigid model of 1.75 mm.

Notes

Acknowledgments

Thanks to the entire team at ImFusion, both for some additional software development supporting this work, as well as providing valuable feedback.

References

  1. 1.
    Xiao, Y., Fortin, M., Unsgård, G., Rivaz, H., Reinertsen, I.: Retrospective evaluation of cerebral tumors (RESECT): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med. Phys. 44, 3875–3882 (2017)CrossRefGoogle Scholar
  2. 2.
    Wein, W., Ladikos, A., Fuerst, B., Shah, A., Sharma, K., Navab, N.: Global registration of ultrasound to MRI using the LC2 metric for enabling neurosurgical guidance. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 34–41. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40811-3_5CrossRefGoogle Scholar
  3. 3.
    Reinertsen, I., Iversen, D., Lindseth, F., Wein, W., Unsgård, G.: Intra-operative ultrasound based correction of brain-shift. In: Intraoperative Imaging Society Conference, Hanover, Germany (2017)Google Scholar
  4. 4.
    De Nigris, D., Collins, L., Arbel, T.: Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations. Int. J. Comput. Assist. Radiol. Surg. 8, 649–661 (2013)CrossRefGoogle Scholar
  5. 5.
    Heinrich, M., et al.: MIND: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16, 1423–1435 (2012)CrossRefGoogle Scholar
  6. 6.
    Jones, D., Perttunen, C., Stuckmann, B.: Lipschitzian optimization without the lipschitz constant. J. Optim. Theory Appl. 79, 157 (1993)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Powell, M.J.: The BOBYQA Algorithm for Bound Constrained Optimization without Derivatives. Cambridge Report NA2009/06, University of Cambridge (2009)Google Scholar
  8. 8.
    Fitzpatrick, J.: Fiducial registration error and target registration error are uncorrelated. In: Miga, M., Wong, I., Kenneth, H. (eds.) Proceedings of the SPIE, vol. 7261 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.ImFusion GmbHMunichGermany

Personalised recommendations