Skip to main content

The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery

International Journal of Computer Assisted Radiology and Surgery volume 10, pages 1127–1140 (2015)Cite this article

Abstract

Purpose

Minimal invasion computer-assisted neurosurgical procedures with various tool insertions into the brain may carry hemorrhagic risks and neurological deficits. The goal of this study is to investigate the role of computer-based surgical trajectory planning tools in improving the potential safety of image-based stereotactic neurosurgery.

Methods

Multi-sequence MRI studies of eight patients who underwent image-guided neurosurgery were retrospectively processed to extract anatomical structures—head surface, ventricles, blood vessels, white matter fibers tractography, and fMRI data of motor, sensory, speech, and visual areas. An experienced neurosurgeon selected one target for each patient. Five neurosurgeons planned a surgical trajectory for each patient using three planning methods: (1) conventional; (2) visualization, in which scans are augmented with overlays of anatomical structures and functional areas; and (3) automatic, in which three surgical trajectories with the lowest expected risk score are automatically computed. For each surgeon, target, and method, we recorded the entry point and its surgical trajectory and computed its expected risk score and its minimum distance from the key structures.

Results

A total of 120 surgical trajectories were collected (5 surgeons, 8 targets, 3 methods). The surgical trajectories expected risk scores improved by 76 % (\(\hbox {SD} =11.6\,\%; p < 0.05\), two-sample student’s t test); the average distance of a trajectory from nearby blood vessels increased by 1.6 mm (\(\hbox {SD}=0.5, p < 0.05\)) from 0.6 to 2.2 mm (243 %). The initial surgical trajectories were changed in 85 % of the cases based on the expected risk score and the trajectory distance from blood vessels.

Conclusions

Computer-based patient-specific preoperative planning of surgical trajectories that minimize the expected risk of vascular and neurological damage due to incorrect tool placement is a promising technique that yields consistent improvements.

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
Fig. 5

References

  1. Grossman R, Sadetzki S, Spiegelmann R, Ram Z (2005) Hemorrhagic complications and the incidence of asymptomatic bleeding associated with stereotactic brain biopsy. Acta Neurochirurgica (Wien) 147:627–631

    CAS  Article  Google Scholar 

  2. Kongham P, Knifed E, Tamber M, Bernstein M (2008) Complications in 622 cases of frame-based stereotactic biopsy, a decreasing procedure. Can J Neurol Sci 35:79–84

    Article  Google Scholar 

  3. MacGirt M, Woodworth G, Coon A, Frazier J, Amundson E, Garonzik I, Olivi A, Weingart J (2005) Independent predictors of morbidity after image-guided stereotactic brain biopsy: a risk assessment of 270 cases. J Neurosurg 102:897–901

    Article  Google Scholar 

  4. Zrinzo L, Foltynie T, Haritz M (2012) Reducing hemorrhagic complications in functional neurosurgery: a large case series and systematic literature review. J Neurosurg 116:84–94

    PubMed  Article  Google Scholar 

  5. Beriault 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 Aid Radiol Surg 7(5):687–704

    Article  Google Scholar 

  6. Bick AS, Mayer A, Levin A (2012) From research to clinical practice: Implementation of functional magnetic imaging and white matter tractography in the clinical environment. J Neurol Sci 312:158–165

    PubMed  Article  Google Scholar 

  7. Lee JD, Huang CH, Lee LT (2002) Improving stereotactic surgery using 3-D reconstruction. IEEE Eng Med Biol Mag 21:109–116

    PubMed  Article  Google Scholar 

  8. Navkar NV, Tsekos NV, Stafford JR, Weinberg JS, Deng Z (2010) Visualization and planning of neurosurgical interventions with straight access. In: Navab N, Jannin P (eds) Proceedings 1st international conference on information processing in computer-assisted interventions. Lecture Notes in Computer Science 6135. Springer, Berlin, pp 1–11

    Google Scholar 

  9. Brunenberg EJ, Vilanova A, Visser-Vandewalle V, Temel Y, Ackermans L, Platel B (2007) Automatic trajectory planning for deep brain stimulation: a feasibility study. In: Ayache N, Ourselin S (eds) Proceedings 10th international conference on medical image computation and computer assisted intervention. Lecture Notes in Computer Science 4791. Springer, Berlin, pp 584–592

    Google Scholar 

  10. 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 Aid Radiol Surg 7(4):517–532

    Article  Google Scholar 

  11. Liu L, Dawant, BM, Pallavaram S, Neimat JS, Konrad PE, D’Haese P, Datteri RD, Landman BA, Noble JH (2012) A surgeon specific automatic path planning algorithm for deep brain stimulation. Proc SPIE Conf 8316:83161A–83161D.

  12. Shamir RR, Joskowicz L, Antiga L, Foroni RI, Shoshan (2010) Trajectory planning method for reduced patient risk in image-guided neurosurgery: concept and preliminary results. Proc SPIE Conf 7625:762520I–7625824

    Article  Google Scholar 

  13. 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 

  14. Vaillant M, Davatzikos C, Taylor RH, Bryan RN (1997) a path-planning algorithm for image-guided neurosurgery. In: Troccaz LJ, Mosges R (eds) Proceedings of 1st joint conference on computer vision, virtual reality and robotics in medicine and medical robotics and computer-assisted surgery. Lecture Notes in Computer Science 1205. Springer, Berlin, pp 467–476

    Google Scholar 

  15. De Momi E, Caborni C, Cardinale F, Casaceli G, Castana L, Cossu M, Mai R, Gozzo F, Francione S, Tassi L, Lo Russo G, Antiga L, Ferrigno G (2014) Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG). Int J CARS (2014) 9:1087–1097. doi:10.1007/s11548-014-1004-1

  16. Liu Y, Konrad P, Neimat J, Tatter S, Yu H, Datteri R, Landman B, Noble J, Pallavaram S, Dawant B, DHaese PF (2014) Multi-surgeon, multi-site validation of a trajectory planning algorithm for deep brain stimulation procedures. IEEE Trans Biomed Eng 61(9):2479–2487. doi: 10.1109/TBME.2014.2322776

  17. Zelmann R, Beriault S, Mok K, Haegelen C, Hall J, Pike GB, Olivier A, Collins DL (2014) Automatic optimization of depth electrode trajectory planning. In: Clinical image-based procedures translational research in medical imaging, pp 99–107

  18. Rincon-Nigro M, Navkar NV, Tsekos NV, Zhigang D (2014) GPU-accelerated interactive visualization and planning of neurosurgical interventions. IEEE Comput Graph Appl 34(1):22–31

    PubMed  Article  Google Scholar 

  19. Shamir R R, Horn M, Blum T, Mehrkens J H, Shoshan Y, Joskowicz L and Navab N (2011) IEEE International Symposium on Biomedical Imaging (ISBI) Trajectory planning with Augmented Reality for improved risk assessment in image-guided keyhole neurosurgery, pp 1873–1876

  20. De Momi E, Carbonary C, Cardinale F, Castana L, Casaceli G, Cossu M, Antiga L, Ferrigno G (2013) Automatic trajectory planner for stereo electro encephalography procedures: a retrospective study. IEEE Trans Biomed Eng 60:986–993

    PubMed  Article  Google Scholar 

  21. Zombori G, Rodinov M, Nowell M, Zuluaga MA, Clarkson MJ, Micallef C, Diehl B, Wehner T, Miserochi A, McEnvoy AW, Duncan JS, Ourselin, S (2014) A computer assisted planning system for the placement of sEEG electrodes in the treatment of epilepsy. In: Stoyanov D et al (eds) Proceedings of 5th international conference on information processing in computer-assisted interventions, IPCAI 2014, Fukuoka, Japan. Lecture Notes in Computer Science 8498, pp 118–127.

  22. Berthold KP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am 4(4):629–642

    Article  Google Scholar 

  23. Wells WM III, Viola P, Atsumi H, Nakajima S, Kikinis R (1996) Multi-modal volume registration by maximization of mutual information. Med Image Anal 1(1):35–51

    PubMed  Article  Google Scholar 

  24. Shamir RR, Freiman M, Joskowicz L, Spektor S, Shoshan Y (2009) Surface-based facial scan registration in neuronavigation procedures: a clinical study. J Neurosurg 111(6):1201–1206

    PubMed  Article  Google Scholar 

  25. Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. Comput Graph 21(4):163–169

  26. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31(3):1116–1128

    PubMed  Article  Google Scholar 

  27. Freiman M, Joskowicz L, Broide N, Natanzon M, Nammer E, Shilon O, Weizman L, Sosna J (2012) Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation. Int J Comput Aid Radiol Surg 7(2):799–812

    CAS  Article  Google Scholar 

  28. MrVista: MATLAB\(^{\rm R}\) interface for analyzing functional and anatomical data. http://white.stanford.edu/newlm/index.php/MrVista. Accessed 18 June 2014

  29. ConTrack: a probabilistic fiber-tracking algorithm. http://white.stanford.edu/newlm/index.php/ConTrack. Accessed 18 June 2014

  30. Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Med 44:625–632

  31. BrainVoyager QX: analysis and visualization of functional and structural magnetic resonance imaging data sets. http://www.brainvoyager.com. Accessed 18 Feb 2014

  32. Friston KJ, Frith C, Frackowiak RSJ, Turner J (1995) Characterizing dynamic brain responses with fMRI: a multivariate approach. NeuroImage 2:166–172

  33. ITK: The Insight Segmentation and Registration Toolkit. http://www.itk.org. Accessed 18 June 2014

  34. MITK: The Medical Imaging Interaction. http://www.mitk.org. Accessed 18 June 2014

  35. VTK: The Visualization Toolkit. http://www.vtk.org. Accessed 18 June 2014

  36. ParaView: Open Source Scientific Visualization. http://www.paraview.org. Accessed 18 June 2014

  37. MATLAB\(^{\rm R}\): Technical Computing. http://www.mathworks.com. Accessed 18 June 2014

Download references

Acknowledgments

Neurosurgeons Zvi Israel, Guy Rosenthal, Iddo Paldor, Fernando Ramirez and Shweiki Moatasim participated in this study.

Conflict of interest

None of the authors has any conflict of interest. The authors have no personal financial or institutional interest in any of the materials, software, or devices described in this article.

Author information

Affiliations

  1. School of Engineering and Computer Science, The Hebrew University of Jerusalem, Givat Ram Campus, 91904, Jerusalem, Israel

    M. Trope & L. Joskowicz

  2. The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University of Jerusalem, Jerusalem, Israel

    R. R. Shamir & L. Joskowicz

  3. Department of Neurosurgery, Center for Functional and Restorative Neurosurgery, Hadassah University Hospital, Ein-Karem, Jerusalem, Israel

    R. R. Shamir

  4. School of Medicine, Stanford University, Palo Alto, CA, USA

    Z. Medress

  5. Department of Neurosurgery, Hadassah University Hospital, Ein-Karem, Jerusalem, Israel

    Z. Medress, G. Rosenthal & Y. Shoshan

  6. fMRI Unit, Department of Neuroradiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel

    A. Mayer, N. Levin & A. Bick

Authors
  1. M. Trope
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. R. Shamir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Joskowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Z. Medress
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. G. Rosenthal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. A. Mayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. N. Levin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. A. Bick
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Y. Shoshan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. Joskowicz.

Additional information

A shorter and restricted version of this paper was orally presented at the 27th International Conference on Computer Aided Radiology and Surgery, Heidelberg, Germany, 2013.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Trope, M., Shamir, R.R., Joskowicz, L. et al. The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery. Int J CARS 10, 1127–1140 (2015). https://doi.org/10.1007/s11548-014-1126-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-014-1126-5

Keywords

  • Stereotactic neurosurgery
  • Surgical trajectory planning
  • Surgical risk reduction
  • Safety
  • Computer-aided planning