Advertisement

Improving Patient Specific Neurosurgical Models with Intraoperative Ultrasound and Augmented Reality Visualizations in a Neuronavigation Environment

  • Ian J. Gerard
  • Marta Kersten-Oertel
  • Simon Drouin
  • Jeffery A. Hall
  • Kevin Petrecca
  • Dante De Nigris
  • Tal Arbel
  • D. Louis Collins
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9401)

Abstract

We present our work to combine intraoperative ultrasound imaging and augmented reality visualization to improve the use of patient specific models throughout image-guided neurosurgery in the context of tumour resections. Preliminary results in a study of 3 patients demonstrate the successful combination of the two technologies as well as improved accuracy of the patient-specific models throughout the surgery. The augmented reality visualizations enabled the surgeon to accurately visualize the anatomy of interest for an extended period of the intervention. These results demonstrate the potential for these technologies to become useful tools for neurosurgeons to improve patient-specific planning by prolonging the use of reliable neuronavigation.

Keywords

Graphic Processing Unit Augmented Reality Registration Error Target Registration Error Brain Shift 
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.

Notes

Acknowledgements

We acknowledged funding support from Canadian Institutes of Health Research (MOP-84360 and MOP-111169), the Canadian National Science and Engineering Research Council (238739) and Brain Canada.

References

  1. 1.
    Roberts, D.W., et al.: A frameless stereotaxic integration of computerized tomographic imaging and the operating microscope. J. Neurosurg. 65(4), 545–549 (1986)CrossRefGoogle Scholar
  2. 2.
    Kersten-Oertel, M., et al.: Augmented reality in neurovascular surgery: Feasibility and first uses in the operating room. Int. J. Comput. Assist. Radiol. Surg. 10(11), 1823–1836 (2015)CrossRefGoogle Scholar
  3. 3.
    Mercier, L., et al.: New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: System description and validation. Int. J. Comput. Assist. Radiol. Surg. 6(4), 507–522 (2011)CrossRefGoogle Scholar
  4. 4.
    Aubert-Broche, B., et al.: A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood. Neuroimage 82, 393–402 (2013)CrossRefGoogle Scholar
  5. 5.
    Coupe, P., et al.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425–441 (2008)CrossRefGoogle Scholar
  6. 6.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar
  7. 7.
    Eskildsen, S.F., Østergaard, L.R.: Active surface approach for extraction of the human cerebral cortex from MRI. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 823–830. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    De Nigris, D., Collins, D.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(4), 649–661 (2013)CrossRefGoogle Scholar
  9. 9.
    Drouin, S., Kersten-Oertel, M., Chen, S.J.-S., Collins, D.: A realistic test and development environment for mixed reality in neurosurgery. In: Linte, C.A., Moore, J.T., Chen, E.C., Holmes III, D.R. (eds.) AE-CAI 2011. LNCS, vol. 7264, pp. 13–23. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Kersten-Oertel, M., et al.: Augmented reality visualization for guidance in neurovascular surgery. Stud. Health Technol. Inform. 173, 225–229 (2012)Google Scholar
  11. 11.
    Caversaccio, M., et al.: Augmented reality endoscopic system (ARES): preliminary results. Rhinology 46(2), 156–158 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ian J. Gerard
    • 1
  • Marta Kersten-Oertel
    • 1
  • Simon Drouin
    • 1
  • Jeffery A. Hall
    • 2
  • Kevin Petrecca
    • 2
  • Dante De Nigris
    • 3
  • Tal Arbel
    • 1
    • 3
  • D. Louis Collins
    • 1
    • 2
    • 3
  1. 1.McConnell Brain Imaging Center, MNIMcGill UniversityMontrealCanada
  2. 2.Department of NeurosurgeryMcGill UniversityMontrealCanada
  3. 3.Centre for Intelligent MachinesMcGill UniversityMontrealCanada

Personalised recommendations