Towards Computer-Assisted Deep Brain Stimulation Targeting with Multiple Active Contacts

  • Silvain Bériault
  • Yiming Xiao
  • Lara Bailey
  • D. Louis Collins
  • Abbas F. Sadikot
  • G. Bruce Pike
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7510)


We present a novel method for preoperative computer-assisted deep brain stimulation (DBS) electrode targeting that takes into account the multiplicity of available contacts and their polarity. Our framework automatically evaluates the efficacy of many possible electrode orientations to optimize the interplay between the extracellular electric field, created from distinct arrangements of active contacts, and anatomical structures responsible for therapeutic and potential side effects. Experimental results on subthalamic DBS cases suggest bipolar configurations provide more flexibility and control on the spread of electric field and, consequently, are most robust to targeting imprecision. Visualization of predicted efficacy maps provides surgeons with complementary feedback that can bridge the gap between insertion safety and optimal therapeutic efficacy. Overall, this work adds a new dimension to preoperative DBS planning and suggests new insights regarding multi-target stimulation.


Deep brain stimulation electric field modeling Parkinson’s disease histological atlas image-guided neurosurgery 


  1. 1.
    Montgomery, E.B.: Deep Brain Stimulation Programming: Principles and Practice. Oxford University Press (2010)Google Scholar
  2. 2.
    Brunenberg, E.J., Platel, B., Hofman, P.A., Ter Haar Romeny, B.M., Visser-Vandewalle, V.: Magnetic resonance imaging techniques for visualization of the subthalamic nucleus. J. Neurosurg. 115, 971–984 (2011)CrossRefGoogle Scholar
  3. 3.
    Nowinski, W.L., Yang, G.L., Yeo, T.T.: Computer-aided stereotactic functional neurosurgery enhanced by the use of the multiple brain atlas database. IEEE Trans. Med. Imaging 19, 62–69 (2000)CrossRefGoogle Scholar
  4. 4.
    D’Haese, P.F., Pallavaram, S., Li, R., Remple, M.S., Kao, C., Neimat, J.S., Konrad, P.E., Dawant, B.M.: CranialVault and its CRAVE tools: A clinical computer assistance system for deep brain stimulation (DBS) therapy. Med. Image Anal. 16, 744–753 (2012)CrossRefGoogle Scholar
  5. 5.
    Elolf, E., Bockermann, V., Gringel, T., Knauth, M., Dechent, P., Helms, G.: Improved visibility of the subthalamic nucleus on high-resolution stereotactic MR imaging by added susceptibility (T2*) contrast using multiple gradient echoes. Am. J. Neuroradiol. 28, 1093–1094 (2007)CrossRefGoogle Scholar
  6. 6.
    Xiao, Y., Beriault, S., Pike, G.B., Collins, D.L.: Multi-contrast multi-echo FLASH MRI for targeting the subthalamic nucleus. Magn. Reson. Imaging 30, 627–640 (2012)CrossRefGoogle Scholar
  7. 7.
    D’Haese, P.F., Cetinkaya, E., Konrad, P.E., Kao, C., Dawant, B.M.: Computer-aided placement of deep brain stimulators: from planning to intraoperative guidance. IEEE Trans. Med. Imaging 24, 1469–1478 (2005)CrossRefGoogle Scholar
  8. 8.
    Guo, T., Parrent, A.G., Peters, T.M.: Surgical targeting accuracy analysis of six methods for subthalamic nucleus deep brain stimulation. Comput. Aided Surg. 12, 325–334 (2007)Google Scholar
  9. 9.
    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. (2011)Google Scholar
  10. 10.
    Bériault, S., Al Subaie, F., 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
  11. 11.
    Pallavaram, S., D’Haese, P.-F., Kao, C., Yu, H., Remple, M., Neimat, J., Konrad, P., Dawant, B.: A New Method for Creating Electrophysiological Maps for DBS Surgery and Their Application to Surgical Guidance. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 670–677. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    McIntyre, C.C., Mori, S., Sherman, D.L., Thakor, N.V., Vitek, J.L.: Electric field and stimulating influence generated by deep brain stimulation of the subthalamic nucleus. Clin. Neurophysiol. 115, 589–595 (2004)CrossRefGoogle Scholar
  13. 13.
    Chakravarty, M.M., Bertrand, G., Hodge, C.P., Sadikot, A.F., Collins, D.L.: The creation of a brain atlas for image guided neurosurgery using serial histological data. Neuroimage 30, 359–376 (2006)CrossRefGoogle Scholar
  14. 14.
    Xiao, Y., Bailey, L., Mallar Chakravarty, M., Beriault, S., Sadikot, A.F., Pike, G.B., Collins, D.L.: Atlas-Based Segmentation of the Subthalamic Nucleus, Red Nucleus, and Substantia Nigra for Deep Brain Stimulation by Incorporating Multiple MRI Contrasts. In: Abolmaesumi, P., Joskowicz, L., Navab, N., Jannin, P. (eds.) IPCAI 2012. LNCS, vol. 7330, pp. 135–145. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Zhang, T.C., Grill, W.M.: Modeling deep brain stimulation: point source approximation versus realistic representation of the electrode. J. Neural. Eng. 7, 1–11 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

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

Personalised recommendations