Skip to main content

Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

International Journal of Computer Assisted Radiology and Surgery volume 9, pages 1087–1097 (2014)Cite this article

Abstract

Purpose

StereoElectroEncephaloGraphy (SEEG) is done to identify the epileptogenic zone of the brain using several multi-lead electrodes whose positions in the brain are pre-operatively defined. Intracranial hemorrhages due to disruption of blood vessels can cause major complications of this procedure (\(<\)1 %). In order to increase the intervention safety, we developed and tested planning tools to assist neurosurgeons in choosing the best trajectory configuration.

Methods

An automated planning method was developed that maximizes the distance of the electrode from the vessels and avoids the sulci as entry points. The angle of the guiding screws is optimized to reduce positioning error. The planner was quantitatively and qualitatively compared with manually computed trajectories on 26 electrodes planned for three patients undergoing SEEG by four neurosurgeons. Quantitative comparison was performed computing for each trajectory using (a) the Euclidean distance from the closest vessel and (b) the incidence angle.

Results

Quantitative evaluation shows that automatic planned trajectories are safer in terms of distance from the closest vessel with respect to manually planned trajectories. Qualitative evaluation performed by four neurosurgeons showed that the automatically computed trajectories would have been preferred to manually computed ones in 30 % of the cases and were judged good or acceptable in about 86 % of the cases. A significant reduction in time required for planning was observed with the automated system (approximately 1/10).

Conclusion

The automatic SEEG electrode planner satisfied the essential clinical requirements, by providing safe trajectories in an efficient timeframe.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Altrogge I, Kröger T, Preusser T, Büskens C, Pereira PL, Schmidt D, Weihusen A, Peitgen HO (2006) Towards optimization of probe placement for radio-frequency ablation. In: Larsen R, Nielsen M, Sporring J (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 4190. Springer, Berlin, pp 486–493

  2. Baegert C, Villard C, Schreck P, Soler L (2007) Precise determination of regions of interest for hepatic RFA planning. Stud Health Technol Inform 125:31–36

    PubMed  Google Scholar 

  3. Baegert C, Villard C, Schreck P, Soler L, Gangi A (2007) Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors. Comput Aided Surg 12(2):82–90

    PubMed  Article  Google Scholar 

  4. Bériault S, Subaie FA, Collins DL, Sadikot AF, Pike GB (2012) A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int J Comput Assist Radiol Surg 7(5):687–704

    PubMed  Article  Google Scholar 

  5. Caborni C, De Momi E, Antiga L, Hammoud A, Ferrigno G, Cardinale F (2012) Automatic trajectory planning in stereo-electroencephalography image guided neurosurgery. Int J Comput Assist Radiol Surg 7(1):S126–S128

    Google Scholar 

  6. Cardinale F, Cossu M, Castana L, Casaceli G, Schiariti M, Miserocchi A, Fuschillo D, Moscato A, Caborni C, Arnulfo G, Lo Russo G (2013) StereoElectroEncephaloGraphy: surgical methodology, safety and stereotactic application accuracy in five hundred procedures. Neurosurgery 72(3):353–366

    PubMed  Article  Google Scholar 

  7. Cardinale F, Cossu M, Castana L, Schiariti M, Miserocchi A, Casaceli G, Caborni C, Moscato A, Lo Russo G (2012) Five hundreds stereoelectroencephalography (SEEG) procedures for epilepsy surgery: a retrospective analysis of clinical safety and in vivo application accuracy. Int J Comput Assist Radiol Surg 7(1):S124–S125

    Google Scholar 

  8. Cardinale F, Miserocchi A, Moscato A, Cossu M, Castana L, Schiariti MP, Gozzo F, Pero G, Quilici L, Citterio A, Minella M, Torresin A, Lo Russo G (2012) Talairach methodology in the multimodal imaging and robotic era. In: Scarabin J (ed) Stereotaxy and epilepsy neurosurgery. John Libbey Eurotext, London, pp 245–272

  9. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis, I: segmentation and surface reconstruction. Neuroimage 9(2):179–194

    PubMed  Article  CAS  Google Scholar 

  10. Danielsson PE (1980) Euclidean Distance Mapping. Comput Graph Image Process 14:227–248

    Article  Google Scholar 

  11. De Momi E, Caborni C, Cardinale F, Castana L, Casaceli G, Cossu M, Antiga L, Ferrigno G (2013) Automatic trajectory planner for StereoElectroEncephaloGraphy procedures: a retrospective study. IEEE Trans Biomed Eng 60(4):968–995

    Google Scholar 

  12. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3):968–980

    PubMed  Article  Google Scholar 

  13. Diehl B, Lüders HO (2000) Temporal lobe epilepsy: when are invasive recordings needed? Epilepsia 41(3):S61–S74

    PubMed  Article  Google Scholar 

  14. Essert C, Haegelen C, Lalys F, Abadie A, Jannin P (2012) Automatic computation of electrode trajectories for Deep Brain Stimulation: a hybrid symbolic and numerical approach. Int J Comput Assist Radiol Surg 7(4):517–532

    PubMed  Article  Google Scholar 

  15. Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378–382

    Article  Google Scholar 

  16. Gering DT, Nabavi A, Kikinis R, Grimson WE, Hata N, Everett P, Jolesz FA, Wells WM (1999) An integrated visualization system for surgical planning and guidance using image fusion and interventional imaging. In: Taylor C, Colchester A (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 1679. Springer, Berlin, pp 809–819

  17. Hoffmann D, Lo Russo G (2008) Stereoelectroencephalography. In: Lüders HO (ed) Textbook of epilepsy surgery. Informa Healthcare, London, pp 945–959

    Chapter  Google Scholar 

  18. Jenkinson M, Bannister PR, Brady JM, Smith SM (2002) Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825–841

    PubMed  Article  Google Scholar 

  19. Kahane P, Landrè E, Minotti L, Francione S, Ryvlin P (2006) The Bancaud and Talairach view on the epileptogenic zone: a working hypothesis. Epileptic Disord 8(2):S16–S26

    PubMed  Google Scholar 

  20. Kilpatrick C, Cook M, Kaye A, Murphy M, Matkovic Z (1997) Non-invasive investigations successfully select patients for temporal lobe surgery. J Neurol Neurosurg Psychiatry 63(3):327–333

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  21. Kimmel R, Sethian JA (1998) Computing geodesic paths on manifolds. Proc Natl Acad Sci 95:8341–8435

    Article  Google Scholar 

  22. Liu Y, Dawant BM, Pallavaram S, Neimat J S, Konrad PE, D’Haese PF, Datteri R, Landman BA, Noble JH (2012) A surgeon specific automatic path planning algorithm for deep brain stimulation. In: Holmes DRIII, Wong KH (eds) Medical imaging 2012: image-guided procedures, robotic interventions, and modeling, SPIE 83161D

  23. Munari C (1987) Depth electrode implantation at Hôpital Sainte Anne, Paris. In: Engel J Jr (ed) Surgical treatment of the epilepsies. Raven Press Ltd, New York, pp 583–588

    Google Scholar 

  24. Munari C, Hoffmann D, Francione S, Kathane P, Tassi L, Lo Russo G, Benabid AL (1994) Stereo-electroencephalography methodology: advantages and limits. Acta Neurol Scand Suppl 152:56–67

    PubMed  Article  CAS  Google Scholar 

  25. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. In: Wells W, Colchester A, Delp S (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS1496. Springer, Berlin, pp 1115–1124

  26. Seitel A, Engel M, Sommer CM, Radeleff BA, Essert-Villard C, Baegert C, Fangerau M, Fritzsche KH, Yung K, Meinzer HP, Maier-Hein L (2011) Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys 38(6):3246–3259

    PubMed  Article  Google Scholar 

  27. Shamir RR, Joskowicz L, Tamir I, Dabool E, Pertman L, Ben-Ami A, Shoshan Y (2012) Reduced risk trajectory planning in image-guided keyhole neurosurgery. Med Phys 39(5):2885–2895

    PubMed  Article  Google Scholar 

  28. Shamir RR, Tamir I, Dabool E, Joskowicz L, Shoshan Y (2010) A method for planning safe trajectories in image-guided keyhole neurosurgery. In: Jiang T, Navab N, Pluim JPW, Viergever MA (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 6363. Springer, Berlin, pp 457–464

  29. Shi Y, Sun B, Lai R, Dinov I, Toga AW (2010) Automated sulci identification via intrinsic modeling of cortical anatomy. In: Jiang T, Navab N, Pluim JPW, Viergever MA (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 6363. Springer, Berlin, pp 49–56

  30. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(1):S208–S219

    Google Scholar 

  31. Spencer SS, Sperling MR, Shewmon AD (1997) Intracranial electrodes. In: Engel J Jr, Pedley TA (eds) Epilepsy: a comprehensive textbook. Lippincott- Raven, Philadelphia, pp 1719–1747

    Google Scholar 

  32. Thadani VM, Williamson PD, Berger R, Spencer SS, Spencer DD, Novelly RA, Sass KJ, Kim JH, Mattson RH (1995) Successful epilepsy surgery without intracranial EEG recording: criteria for patient selection. Epilepsia 36(1):7–15

    PubMed  Article  CAS  Google Scholar 

  33. Viola P, Wells WM (1997) Alignment by maximization of mutual information. Int J Comp Vis 24(2):137–154

    Article  Google Scholar 

  34. Vitter JS (1984) Faster methods for random sampling. Commun ACM 27(7):703–718

    Article  Google Scholar 

  35. Yuan J, Chen Y, Hirsch E (2012) Intracranial electrodes in the presurgical evaluation of epilepsy. Neurol Sci 33:723–729

    PubMed  Article  Google Scholar 

  36. Zumsteg D, Wiese HG (2000) Presurgical evaluation: current role of invasive EEG. Epilepsia 41(3):S55–S60

    PubMed  Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the EU project ACTIVE FP7 ICT 270460, by Renishaw mayfield (Switzerland) and by Renishaw (UK).

Conflict of interest

Author F.C. is a consultant to Renishaw-mayfield, the manufacturer of Neuromate robotic system, and a former consultant to Medtronic, the manufacturer of the O-arm.

Author information

Affiliations

  1. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy

    E. De Momi, C. Caborni & G. Ferrigno

  2. “Claudio Munari” Centre for Epilepsy and Parkinson Surgery Ospedale Niguarda Ca’ Granda, Milan, Italy

    F. Cardinale, G. Casaceli, L. Castana, M. Cossu, R. Mai, F. Gozzo, S. Francione, L. Tassi & G. Lo Russo

  3. Orobix Srl, Bergamo, Italy

    L. Antiga

Authors
  1. E. De Momi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Caborni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Cardinale
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Casaceli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Castana
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Cossu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. R. Mai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. F. Gozzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. S. Francione
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. L. Tassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. G. Lo Russo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. L. Antiga
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. G. Ferrigno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. De Momi.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

De Momi, E., Caborni, C., Cardinale, F. et al. Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG). Int J CARS 9, 1087–1097 (2014). https://doi.org/10.1007/s11548-014-1004-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-014-1004-1

Keywords

  • Computer-assisted surgery
  • Epilepsy
  • StereoElectroEncephaloGraphy