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Automatic Optimization of Depth Electrode Trajectory Planning

  • Rina ZelmannEmail author
  • Silvain Beriault
  • Kelvin Mok
  • Claire Haegelen
  • Jeff Hall
  • G. Bruce Pike
  • Andre Olivier
  • D. Louis Collins
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8361)

Abstract

This paper presents a fully automatic procedure for optimization of depth electrode implantation planning in epilepsy. To record intracranial EEG in some patients with intractable epilepsy, depth electrodes are implanted through holes in the skull. The proposed fully automatic procedure maximizes recording coverage of the target volume by estimating the EEG recorded from each contact, while minimizing the risk of approaching vessels and other critical structures. All structures, including the hippocampus and amygdala were automatically segmented. We retrospectively validated the procedure for mesial temporal lobe implantations in 11 hemispheres. The automatic trajectories recorded from a larger volume of interest than the original manually selected trajectories while better avoiding the segmented structures. The procedure is integrated into a neuronavigation system enabling the surgeon to visualize the selected trajectories from an ordered list and, if necessary, enables re-planning a trajectory in near real time.

Keywords

Depth electrode implantation Trajectory optimization EEG recording maximization Automatic target segmentation Image guided neurosurgery 

Notes

Acknowledgements

This study was supported in part by CIHR MOP-97820 and by MNI CIBC postdoctoral fellowship in brain imaging.

References

  1. 1.
    Engel, J., Pedley, T.A.: Epilepsy: A Comprehensive Textbook. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia (2008)Google Scholar
  2. 2.
    Bériault, S., Subaie, F.A., Lalys, F., Collins, D.L., Pike, G.B., Sadikot, A.F.: A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int. J. Comput. Assist. Radiol. Surg. 7, 1–18 (2012)CrossRefGoogle Scholar
  3. 3.
    Essert, C., Haegelen, C., Lalys, F., Abadie, A., Jannin, P.: Automatic computation of electrode trajectories for deep brain stimulation: a hybrid symbolic and numerical approach. Int. J. Comput. Assist. Radiol. Surg. 7, 517–532 (2012)CrossRefGoogle Scholar
  4. 4.
    Guo, T., Parrent, A.G., Peters, T.M.: Automatic target and trajectory identification for deep brain stimulation (DBS) procedures. In: Ayache, Nicholas, Ourselin, Sébastien, Maeder, Anthony (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 483–490. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Liu, Y., et al.: A surgeon specific automatic path planning algorithm for deep brain stimulation. In: Proceedings of the SPIE 8316 Medical Imaging 2011, p. 83161D (2011)Google Scholar
  6. 6.
    Seitel, A., et al.: Computer-assisted trajectory planning for percutaneous needle insertions. Med. Phys. 38, 3246–3259 (2011)CrossRefGoogle Scholar
  7. 7.
    De Momi, E., Caborni, C., Cardinale, F., Castana, L., Casaceli, G., Cossu, M., Antiga, L., Ferrigno, G.: Automatic trajectory planner for StereoElectroEncephaloGraphy procedures: a retrospective study. IEEE Trans. Biomed. Eng. 4, 986–993 (2013)CrossRefGoogle Scholar
  8. 8.
    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, 507–522 (2011)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Nyul, L.G., Udupa, J.K., Saha, P.K.: Incorporating a measure of local scale in voxel-based 3-D image registration. IEEE Trans. Med. Imaging 22, 228–237 (2003)CrossRefGoogle Scholar
  11. 11.
    Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., et al.: A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1293–1322 (2001)CrossRefGoogle Scholar
  12. 12.
    Eskildsen, S.F.: BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 59, 2362–2373 (2012)CrossRefGoogle Scholar
  13. 13.
    Collins, D.L., Pruessner, J.C.: Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. NeuroImage 52, 1355–1366 (2010)CrossRefGoogle Scholar
  14. 14.
    Collins, D.L., Zijdenbos, A., Baaré, W., Evans, A.: ANIMAL+INSECT: improved cortical structure segmentation. In: Kuba, A., Šáamal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 210–223. Springer, Heidelberg (1999)Google Scholar
  15. 15.
    Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27, 425–441 (2008)CrossRefGoogle Scholar
  16. 16.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)Google Scholar
  17. 17.
    Danielsson, P.E.: Euclidean distance mapping. Comput. Graph. Image Process. 14, 227–248 (1980)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rina Zelmann
    • 1
    Email author
  • Silvain Beriault
    • 1
  • Kelvin Mok
    • 1
  • Claire Haegelen
    • 1
  • Jeff Hall
    • 1
  • G. Bruce Pike
    • 1
  • Andre Olivier
    • 1
  • D. Louis Collins
    • 1
  1. 1.Neurology and NeurosurgeryMontreal Neurological InstituteMontrealCanada

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