Advertisement

A Prospective Evaluation of Computer-Assisted Deep Brain Stimulation Trajectory Planning

  • Silvain Bériault
  • Simon Drouin
  • Abbas F. Sadikot
  • Yiming Xiao
  • D. Louis Collins
  • G. Bruce Pike
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7761)

Abstract

Careful planning of deep brain stimulation (DBS) insertion trajectories is key to minimizing risks of surgery-related complications such as hemorrhages, cerebrospinal fluid leakage and loss of function. Recently, some computer-assisted frameworks were proposed and retrospectively validated to demonstrate superior optimization of many surgical constraints in comparison to manual trajectory planning by the neurosurgeon. However, limited data is available on the assessment of whether these computed trajectories prospectively translate to surgical lead insertions. This work presents the clinical integration of a prototype frameless neuronavigation platform and of a new software module, named CAPS (Computer-Assisted Path-planning Software), within the overall DBS surgical workflow. A prospective evaluation on 8 DBS cases reveals that the use of CAPS can influence the surgeon’s decision-making. For 7 out of 8 cases, the surgeon performed the lead insertion based on a surgical plan obtained using CAPS and 3 of these plans differed significantly, in lead orientation, from those identified manually using an FDA-approved Medtronic StealthStation® system.

Keywords

Deep brain stimulation image-guided neurosurgery computerassisted planning Parkinson’s disease 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Benabid, A.L., Chabardes, S., Mitrofanis, J., Pollak, P.: Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson’s disease. Lancet Neurol 8, 67–81 (2009)CrossRefGoogle Scholar
  2. 2.
    Vaillant, M., Davatzikos, C., Taylor, R., Bryan, R.: A path-planning algorithm for image-guided neurosurgery. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997. LNCS, vol. 1205, pp. 467–476. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  3. 3.
    Fujii, T., Emoto, H., Sugou, N., Mito, T., Shibata, I.: Neuropath planner-automatic path searching for neurosurgery. International Congress Series 1256, 587–596 (2003)CrossRefGoogle Scholar
  4. 4.
    Brunenberg, E.J.L., Vilanova, A., Visser-Vandewalle, V., Temel, Y., Ackermans, L., Platel, B., ter Haar Romeny, B.M.: Automatic Trajectory Planning for Deep Brain Stimulation: A Feasibility Study. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 584–592. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Shamir, R.R., Tamir, I., Dabool, E., Joskowicz, L., Shoshan, Y.: A Method for Planning Safe Trajectories in Image-Guided Keyhole Neurosurgery. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 457–464. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Bériault, S., Subaie, F.A., Mok, K., Sadikot, A.F., Pike, G.B.: Automatic Trajectory Planning of DBS Neurosurgery from Multi-Modal MRI Datasets. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 259–266. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Beriault, S., Al Subaie, F., Collins, D.L., Sadikot, A.F., Pike, G.B.: A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int. J. Comput. Assist. Radiol. Surg. 7, 687–704 (2012)CrossRefGoogle Scholar
  9. 9.
    Mercier, L., Del Maestro, R.F., Petrecca, K., Kochanowska, A., Drouin, S., Yan, C.X., Janke, A.L., Chen, S.J., Collins, D.L.: 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
  10. 10.
    Collins, D.L., Zijdenbos, A.P., Baaré, W.F.C., Evans, A.C.: ANIMAL+INSECT: Improved Cortical Structure Segmentation. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 210–223. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  11. 11.
    Frangi, A., Niessen, W., Vincken, K., Viergever, M.: 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)CrossRefGoogle Scholar
  12. 12.
    Drouin, S., Kersten-Oertel, M., Chen, S., Collins, D.L.: A Realistic Test and Development Environment for Mixed Reality in Neurosurgery. In: Linte, C.A., Moore, J.T., Chen, E.C.S., Holmes III, D.R. (eds.) AE-CAI 2011. LNCS, vol. 7264, pp. 13–23. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Pfisterer, W.K., Papadopoulos, S., Drumm, D.A., Smith, K., Preul, M.C.: Fiducial versus nonfiducial neuronavigation registration assessment and considerations of accuracy. Neurosurgery 62, 201–207 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Silvain Bériault
    • 1
  • Simon Drouin
    • 1
  • Abbas F. Sadikot
    • 1
  • Yiming Xiao
    • 1
  • D. Louis Collins
    • 1
  • G. Bruce Pike
    • 1
  1. 1.McConnell Brain Imaging CentreMontreal Neurological InstituteMontrealCanada

Personalised recommendations