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Statistical study of parameters for deep brain stimulation automatic preoperative planning of electrodes trajectories

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Automatic methods for preoperative trajectory planning of electrodes in deep brain stimulation are usually based on the search for a path that resolves a set of surgical constraints to propose an optimal trajectory. The relative importance of each surgical constraint is usually defined as weighting parameters that are empirically set beforehand. The objective of this paper is to analyze the use of these parameters thanks to a retrospective study of trajectories manually planned by neurosurgeons. For that purpose, we firstly retrieved weighting factors allowing to match neurosurgeons manually planned choice of trajectory on each retrospective case; secondly, we compared the results from two different hospitals to evaluate their similarity; and thirdly, we compared the trends to the weighting factors empirically set in most current approaches.

Methods

To retrieve the weighting factors best matching the neurosurgeons manual plannings, we proposed two approaches: one based on a stochastic sampling of the parameters and the other on an exhaustive search. In each case, we obtained a sample of combinations of weighting parameters with a measure of their quality, i.e., the similarity between the automatic trajectory they lead to and the one manually planned by the surgeon as a reference. Visual and statistical analyses were performed on the number of occurrences and on the rank means.

Results

We performed our study on 56 retrospective cases from two different hospitals. We could observe a trend of the occurrence of each weight on the number of occurrences. We also proved that each weight had a significant influence on the ranking. Additionally, we observed no influence of the medical center parameters, suggesting that the trends were comparable in both hospitals. Finally, the obtained trends were confronted to the usual weights chosen by the community, showing some common points but also some discrepancies.

Conclusion

The results tend to show a predominance of the choice of a trajectory close to a standard direction. Secondly, the avoidance of the vessels or sulci seems to be sought in the surroundings of the standard position. The avoidance of the ventricles seems to be less predominant, but this could be due to the already reasonable distance between the standard direction and the ventricles. The similarity of results between two medical centers tends to show that it is not an exceptional practice. These results suggest that manual planning software may introduce a bias in the planning by proposing a standard position.

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References

  1. Baegert C, Essert-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

    Article  PubMed  Google Scholar 

  2. Benabid A, Chabardes S, Mitrofanis J, Pollak P (2009) DBS of the subthalamic nucleus for the treatment of Parkinson’s disease. Lancet Neurol 8(1):67–81

    Article  PubMed  Google Scholar 

  3. Bériault S, Drouin S, Sadikot AF, Xiao Y, Collins DL, Pike GB (2013) A prospective evaluation of computer-assisted deep brain stimulation trajectory planning. In: Proceedings of CLIP’12, Springer LNCS, vol 7761, pp 42–49

  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

    Article  PubMed  Google Scholar 

  5. D’Albis T, Haegelen C, Essert C, Fernandez-Vidal S, Lalys F, Jannin P (2014) PyDBS: an automated image processing workflow for deep brain stimulation surgery. Int J Comput Assist Radiol Surg 10(2):117–128

    Article  PubMed  Google Scholar 

  6. D’Haese PF, Pallavaram S, Li R, Remple MS, Kao C, Neimat JS, Konrad PE, Dawant BM (2012) CranialVault and its CRAVE tools: a clinical computer assistance system for deep brain stimulation (DBS) therapy. Med Image Anal 16(3):744–753

    Article  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  8. Essert C, Marchal M, Fernandez-Vidal S, D’Albis T, Bardinet E, Haegelen C, Welter ML, Yelnik J, Jannin P (2012) Automatic parameters optimization for deep brain stimulation trajectory planning. In: Proceedings of MICCAI workshop DBSMC’12, pp. 20–29

  9. Limousin P, Krack P, Pollak P, Benazzouz A, Ardouin C, Hoffmann D, Benabid AL (1998) Electrical stimulation of the subthalamic nucleus in advanced Parkinson’s disease. New Engl J Med 339(16):1105–1111

    Article  CAS  PubMed  Google Scholar 

  10. Liu Y, Dawant BM, Pallavaram S, Neimat JS, Konrad PE, D’Haese PF, Datteri RD, Landman BA, Noble JH (2012) A surgeon specific automatic path planning algorithm for deep brain stimulation. In: Proceedings of SPIE medical imaging 2012: image-guided procedures, robotic interventions, and modeling, p. 83161D

  11. Liu Y, Konrad P, Neimat J, Tatter S, Yu H, Datteri R, Landman B, Noble J, Pallavaram S, Dawant B, D’Haese PF (2014) Multisurgeon, multisite validation of a trajectory planning algorithm for deep brain stimulation procedures. IEEE Trans Biomed Eng 61(9):2479–2487

    Article  PubMed Central  PubMed  Google Scholar 

  12. Machado A, Rezai AR, Kopell BH, Gross RE, Sharan AD, Benabid AL (2006) Deep brain stimulation for Parkinson’s disease: surgical technique and perioperative management. Mov Disord 21(S14):S247–S258

    Article  PubMed  Google Scholar 

  13. Shamir R, Tamir I, Dabool E, Joskowicz L, Shoshan Y (2010) A method for planning safe trajectories in image-guided keyhole neurosurgery. In: Proceedings of MICCAI’10, Springer LNCS, vol 6363, pp 457–464

  14. Tirelli P, de Momi E, Borghese N, Ferrigno G (2009) An intelligent atlas-based planning system for keyhole neurosurgery. In: Computer assisted radiology and surgery supplemental, pp S85–S91

  15. York MK, Wilde EA, Simpson R, Jankovic J (2009) Relationship between neuropsychological outcome and DBS surgical trajectory and electrode location. J Neurol Sci 287(1–2):159–171

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

The authors thank the French National Research Agency (ANR) for funding this work through the ACouStiC project grant (ANR 2010 BLAN 0209 02).

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Correspondence to Caroline Essert.

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The authors declare that they have no conflict of interest.

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In this paper, a retrospective study has been performed on a dataset of anonymized images, but informed consent was obtained from all individual participants included in the study.

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Essert, C., Fernandez-Vidal, S., Capobianco, A. et al. Statistical study of parameters for deep brain stimulation automatic preoperative planning of electrodes trajectories. Int J CARS 10, 1973–1983 (2015). https://doi.org/10.1007/s11548-015-1263-5

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  • DOI: https://doi.org/10.1007/s11548-015-1263-5

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