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Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated

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Abstract

Purpose

Deep brain stimulation (DBS) is a procedure requiring accurate targeting and electrode placement. The two key elements for successful planning are preserving patient safety by ensuring a safe trajectory and creating treatment efficacy through optimal selection of the stimulation point. In this work, we present the first approach of computer-assisted preoperative DBS planning to automatically optimize both the safety of the electrode’s trajectory and location of the stimulation point so as to provide the best clinical outcome.

Methods

Building upon the findings of previous works focused on electrode trajectory, we added a set of constraints guiding the choice of stimulation point. These took into account retrospective data represented by anatomo-clinical atlases and intersections between the stimulation region and sensitive anatomical structures causing side effects. We implemented our method into automatic preoperative planning software to assess if the algorithm was able to simultaneously optimize electrode trajectory and the stimulation point.

Results

Leave-one-out cross-validation on a dataset of 18 cases demonstrated an improvement in the expected outcome when using the new constraints. The distance to critical structures was not reduced. The intersection between the stimulation region and structures sensitive to stimulation was minimized.

Conclusions

Introducing these new constraints guided the planning to select locations showing a trend toward symptom improvement, while minimizing the risks of side effects, and there was no cost in terms of trajectory safety.

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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. Baegert C, Villard C, Schreck P, Soler L (2007) Multi-criteria trajectory planning for hepatic radiofrequency ablation. In: Proceedings of MICCAI’07. Springer LNCS, vol 4791, pp 584–592

  3. Baker KB, Boulis NM, Rezai AR, Montgomery EB Jr (2004) Target selection using microelectrode recording. In: Israel Z, Burchiel KJ (eds) Microelectrode recordings in movement disorder surgery. Thieme, New York, pp 138–151

    Google Scholar 

  4. 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

  5. Bériault S, Subaie F, Mok K, Sadikot A, Pike GB (2011) Automatic trajectory planning of DBS neurosurgery from multi-modal MRI datasets. In: Proceedings of MICCAI’11. Springer LNCS, vol 6891, pp 259–266

  6. Bériault S, Xiao Y, Bailey L, Collins D, Sadikot A, Pike G (2012) Towards computer-assisted deep brain stimulation targeting with multiple active contacts. In: Proceedings of MICCAI’12. Springer LNCS, vol 7510, pp 487–494

  7. Bourbakis N, Awad M (2003) A 3-D visualization method for image-guided brain surgery. IEEE Trans Syst Man Cybern B Cybern 33(5):766–781

    Article  PubMed  CAS  Google Scholar 

  8. Brunenberg E, Vilanova A, Visser-Vandewalle V, Temel Y, Ackermans L, Platel B, ter Haar Romeny B (2007) Automatic trajectory planning for deep brain stimulation: a feasibility study. In: Proceedings of MICCAI’07. Springer LNCS, vol 4791, pp 584–592

  9. Butson CR, McIntyre CC (2005) Role of electrode design on the volume of tissue activated during deep brain stimulation. J Neural Eng 3(1):1

    Article  PubMed  PubMed Central  Google Scholar 

  10. Butson C, Cooper S, Henderson J, McIntyre C (2007) Patient-specific analysis of the volume of tissue activated during deep brain stimulation. NeuroImage 34:661–670

    Article  PubMed  Google Scholar 

  11. Chaturvedi A, Butson CR, Lempka SF, Cooper SE, McIntyre CC (2010) Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions. Brain Stimul 3(2):65–77

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chaturvedi A, Luján J, McIntyre C (2013) Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation. J Neural Eng 10(5):056,023

    Article  Google Scholar 

  13. 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, pp 1–12

  14. 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 

  15. Essert C, Fernandez-Vidal S, Capobianco A, Haegelen C, Karachi C, Bardinet E, Marchal M, Jannin P (2015) Statistical study of parameters for deep brain stimulation automatic preoperative planning of electrodes trajectories. Int J Comput Assist Radiol Surg. https://doi.org/10.1007/s11548-015-1263-5

    Article  PubMed  Google Scholar 

  16. Fujii T, Emoto H, Sugou N, Mito T, Shibata I (2003) Neuropath planner-automatic path searching for neurosurgery. In: Proceedings of CARS’03. Elsevier, vol 1256, pp 587–596

  17. Haegelen C, Coupé P, Fonov V, Guizard N, Jannin P, Morandi X, Collins DL (2013) Automated segmentation of basal ganglia and deep brain structures in mri of Parkinson’s disease. Int J Comput Assist Radiol Surg 8(1):99–110

    Article  PubMed  Google Scholar 

  18. Hamze N, Voirin J, Collet P, Jannin P, Haegelen C, Essert C (2016) Pareto front vs. weighted sum for automatic trajectory planning of deep brain stimulation. In: Medical image computing and computer assisted intervention (MICCAI’16), LNCS. Springer, vol 9900, pp 534–541

  19. Herzog J, Fietzek U, Hamel W, Morsnowski A, Steigerwald F, Schrader B, Weinert D, Pfister G, Muller D, Mehdorn H, Deuschl G, Volkmann J (2004) Most effective stimulation site in subthalamic deep brain stimulation for Parkinson’s disease. Mov Disord 19(9):1050–1099

    Article  PubMed  Google Scholar 

  20. Jaberzadeh A, Essert C (2014) Multi-probe three-dimensional placement planning for liver cryosurgery: comparison of different optimization methods. In: Proceedings of CMMSE’14, vol 3, pp 743–754

  21. Lalys F, Haegelen C, Maroua M, Drapier S, Verin M, Jannin P (2013) Anatomo-clinical atlases correlate clinical data and electrode contact coordinates: application to subthalamic deep brain stimulation. J Neurosci Methods 212(2):297–307

    Article  PubMed  Google Scholar 

  22. Lalys F, Haegelen C, Abadie A, Jannin P (2009) Post-operative assessment in deep brain stimulation based on multimodal images: registration workflow and validation. In: Proceedings of SPIE medical imaging, vol 7261(1), p 72612M

  23. Lee J, Huang C, Lee S (2002) Improving stereotactic surgery using 3-D reconstruction. IEEE Eng Med Biol Mag 21(6):109–116

    Article  PubMed  Google Scholar 

  24. 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  PubMed Central  Google Scholar 

  25. Maks C, Butson C, Walter B, Vitek J, McIntyre CC (2009) Deep brain stimulation activation volumes and their association with neurophysiological mapping and therapeutic outcomes. J Neurol Neurosurg Psychiatry 80:659–666

    Article  PubMed  CAS  Google Scholar 

  26. Mehri M, Lalys F, Maumet C, Haegelen C, Jannin P (2012) Analysis of electrodes’ placement and deformation in deep brain stimulation from medical images. In: Proceedings of SPIE medical imaging, vol 8316(1), p 831632

  27. Mikos A, Bowers D, Noecker A, McIntyre C, Won M, Chaturvedi A, Foote K, Okun M (2011) Patient-specific analysis of the relationship between the volume of tissue activated during dbs and verbal fluency. Neuroimage 54S1:238–246

    Article  Google Scholar 

  28. Navkar N, Tsekos N, Stafford J, Weinberg J, Deng Z (2010) Visualization and planning of neurosurgical interventions with straight access. In: Proceedings of IPCAI’10. Springer LNCS, vol 6135, pp 1–11

  29. Nelder J, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313. https://doi.org/10.1093/comjnl/7.4.308

    Article  Google Scholar 

  30. Nowinski W, Yang G, Yeo T (2002) Computer-aided stereotactic functional neurosurgery enhanced by the use of the multiple brain atlas database. IEEE Trans Med Imaging 19(1):62–69

    Article  Google Scholar 

  31. Pallavaram S, D’Haese PF, Lake W, Konrad PE, Dawant BM, Neimat JS (2015) Fully automated targeting using non-rigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson’s disease. Neurosurgery 76(6):756–765

    Article  PubMed  PubMed Central  Google Scholar 

  32. Saint-Cyr J, Hoque T, Pereira L, Dostrovsky J, Hutchison W, Mikulis D, Abosch A, Sime E, Lang A, Lozano A (2002) Localization of clinically effective stimulating electrodes in the human subthalamic nucleus on magnetic resonance imaging. J Neurosurg 97:1152–1166

    Article  PubMed  Google Scholar 

  33. 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

  34. 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

  35. Tisch S, Zrinzo L, Limousin P, Bhatia K, Quinn N, Ashkan K, Hariz M (2007) Effect of electrode contact location on clinical efficacy of pallidal deep brain stimulation in primary generalised dystonia. J Neurol Neurosurg Psychiatry 78:1314–1319

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Vaillant M, Davatzikos C, Taylor R, Bryan R (1997) A path-planning algorithm for image-guided neurosurgery. In: Proceedings of CVRMed-MRCAS’97. Springer LNCS, vol 1205, pp 467–476

  37. Zhang TC, Grill WM (2010) Modeling deep brain stimulation: point source approximation versus realistic representation of the electrode. J Neural Eng 7(6):066,009

    Article  Google Scholar 

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Acknowledgements

This work was funded by the French National Research Agency (ANR) through the ACouStiC Project Grant (ANR 2010 BLAN 0209 02).

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Authors and Affiliations

  1. ICube Laboratory, CNRS, UMR 7357, Université de Strasbourg, Strasbourg, France

    Olga Dergachyova & Caroline Essert

  2. INSERM, U1099, Rennes, 35000, France

    Olga Dergachyova, Yulong Zhao, Claire Haegelen & Pierre Jannin

  3. Université de Rennes 1, LTSI, Rennes, 35000, France

    Olga Dergachyova, Yulong Zhao, Claire Haegelen & Pierre Jannin

  4. CHU Rennes, Service de Neurochirurgie, Rennes, 35000, France

    Claire Haegelen

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  1. Olga Dergachyova
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  2. Yulong Zhao
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  3. Claire Haegelen
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  4. Pierre Jannin
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  5. Caroline Essert
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Correspondence to Olga Dergachyova.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Dergachyova, O., Zhao, Y., Haegelen, C. et al. Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated. Int J CARS 13, 1117–1128 (2018). https://doi.org/10.1007/s11548-018-1724-8

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  • DOI: https://doi.org/10.1007/s11548-018-1724-8

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