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Targeting Epilepsy Through the Foremen Ovale: How Many Helical Needles are Needed?

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Abstract

Laser ablation of the hippocampus offers medically refractory epilepsy patients an alternative to invasive surgeries. Emerging commercial solutions deliver the ablator through a burr hole in the back of the head. We recently introduced a new access path through the foremen ovale, using a helical needle, which minimizes the amount of healthy brain tissue the needle must pass through on its way to the hippocampus, and also enables the needle to follow the medial axis of the hippocampus more closely. In this paper, we investigate whether helical needles should be designed and fabricated on a patient-specific basis as we had previously proposed, or whether a small collection of pre-defined needle shapes can apply across many patients. We propose a new optimization strategy to determine this needle set using patient data, and investigate the accuracy with which these needles can reach the the medial axis of the hippocampus. We find that three basic tube shapes (mirrored as necessary for left vs. right hippocampi) are all that is required, across 20 patient datasets (obtained from 10 patient CT scans), to reduce worst-case maximum error below 2 mm.

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References

  1. Baykal, C., L.G. Torres, R. Alterovitz. Optimizing design parameters for sets of concentric tube robots using sampling-based motion planning Cenk. In: The Encyclopedia of Medical Robotics, pp. 4381–4387, 2015.

  2. Bergeles, C., A. H. Gosline, N. V. Vasilyev, P. J. Codd, P. J. del Nido, and P. E. Dupont. Concentric tube robot design and optimization based on task and anatomical constraints. IEEE Trans. Robot. 31:67–84, 2015.

    Article  Google Scholar 

  3. Boushaki, M., C. Liu, B. Herman, V. Trevillot, M. Akkari, and P. Poignet. Optimization of concentric-tube robot design for deep anterior brain tumor surgery. In: International Conference on Control, Automation, Robotics & Vision, pp. 1–6, 2016.

  4. Comber, D. B., E. J. Barth, and R. J. Webster. Design and control of an magnetic resonance compatible precision pneumatic active cannula robot. J. Med. Devices 8:1–7, 2013.

    Google Scholar 

  5. Comber, D. B., E. B. Pitt, H. B. Gilbert, M. W. Powelson, E. Matijevich, J. S. Neimat, R. J. Webster, and E. J. Barth. Optimization of curvilinear needle trajectories for transforamenal hippocampotomy. Oper. Neurosurg. 13:15–21, 2017.

    Article  Google Scholar 

  6. Comber, D. B., J. E. Slightam, V. R. Gervasi, J. S. Neimat, and E. J. Barth. Design, additive manufacture, and control of a pneumatic MR-compatible needle driver. IEEE Trans. Robot. 32:138–149, 2016.

    Article  Google Scholar 

  7. Curry, D. J., A. Gowda, R. J. McNichols, and A. A. Wilfong. MR-guided stereotactic laser ablation of epileptogenic foci in children. Epilepsy Behav. 24:408–414, 2012.

    Article  Google Scholar 

  8. De Flon, P., E. Kumlien, C. Reuterwall, and P. Mattsson. Empirical evidence of underutilization of referrals for epilepsy surgery evaluation. Eur. J. Neurol. 17:619–625, 2010.

    Article  Google Scholar 

  9. Engel, J., S. Wiebe, J. French, M. Sperling, P. Williamson, D. Spencer, R. Gumnit, C. Zahn, E. Westbrook, and B. Enos. Practice parameter: temporal lobe and localized neocortical resections for epilepsy—Report of the quality standards subcommittee of the American Academy of Neurology, in association with the American Epilepsy Society and the American Association of Neur. Neurology 60:538–547, 2003.

    Article  Google Scholar 

  10. Garriga-Casanovas, A., and F. Rodriguez y Baena. Complete follow-the-leader kinematics using concentric tube robots. Int. J. Robot. Res. 37:197–222, 2018.

  11. Gilbert, H. B., J. Neimat, and R. J. Webster III. Concentric tube robots as steerable needles: achieving follow-the-leader deployment. IEEE Trans. Robot. 31:246–258, 2015.

    Article  Google Scholar 

  12. Gilbert, H. B. and R. J. Webster. Rapid, reliable shape setting of Ssuperelastic nitinol for prototyping robots. IEEE Robot. Autom. Lett. 1:98–105, 2016.

    Article  Google Scholar 

  13. Granna, J., T. S. Rau, T.-D. Nguyen, T. Lenarz, O. Majdani, and J. Burgner-Kahrs. Toward automated cochlear implant insertion using tubular manipulators. In: Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 9786, p. 97861F, 2016.

  14. Ha, J., F. C. Park, and P. E. Dupont. Optimizing tube precurvature to enhance the elastic stability of concentric tube robots. IEEE Trans. Robot. 33:22–37, 2017.

    Article  Google Scholar 

  15. Hawasli, A. H., K. S. Bandt, R. E. Hogan, N. Werner, and E. C. Leuthardt. Laser ablation as treatment strategy for medically refractory dominant insular epilepsy—therapeutic and functional considerations. Stereotact. Funct. Neurosurg. 92:397–404, 2014.

    Article  Google Scholar 

  16. Hori, T., F. Yamane, T. Ochiai, M. Hayashi, and T. Taira. Subtemporal amygdalohippocampectomy prevents verbal memory impairment in the language-dominant hemisphere. Stereotact. Funct. Neurosurg. 80:18–21, 2003.

    Article  Google Scholar 

  17. Johnson, H. J., G. Harris, and K. Williams. BRAINSFit: mutual information rigid registrations of whole-brain 3D images, using the insight toolkit. OR Insight 57(1):1–10, 2007.

    Google Scholar 

  18. Kwan, P. and M. J. Brodie. Early identification of refractory epilepsy. New Engl. J. Med. 342:314–319, 2000.

    Article  CAS  Google Scholar 

  19. Landazuri, P., J. Shih, E. Leuthardt, S. Ben-Haim, J. Neimat, Z. Tovar-Spinoza, V. Chiang, D. Spencer, D. Sun, P. Fecci, and J. Baumgartner. A prospective multicenter study of laser ablation for drug resistant epilepsy—one year outcomes. Epilepsy Res. 167:106473, 2020.

    Article  Google Scholar 

  20. Lee, T.-C. and R. Kashyap. Building skeleton models via 3D medical surface/axis thinning algorithms. Graph. Models Image Process. 56:462–478, 1994.

    Article  Google Scholar 

  21. Lutz, M. T., H. Clusmann, C. E. Elger, J. Schramm, and C. Helmstaedter. Neuropsychological outcome after selective amygdalohippocampectomy with transsylvian versus transcortical approach: a randomized prospective clinical trial of surgery for temporal lobe epilepsy. Epilepsia 45:809–816, 2004.

    Article  Google Scholar 

  22. Mahoney, A. W., H. B. Gilbert, and R. J. Webster III. A review of concentric tube robots: modeling, control, design, planning, and sensing. In: The Encyclopedia of Medical Robotics. World Scientific, Singapore, pp. 181–202, 2018.

    Google Scholar 

  23. Peyron, Q., K. Rabenorosoa, N. Andreff, and P. Renaud. A numerical framework for the stability and cardinality analysis of concentric tube robots: introduction and application to the follow-the-leader deployment. Mech. Mach. Theory 132:176–192, 2019.

    Article  Google Scholar 

  24. Pourafzal, M., H. A. Talebi, and K. Rabenorosoa. Piecewise constant strain kinematic model of externally loaded concentric tube robots. Mechatronics 74(6):102502, 2021.

  25. Uijl, S. G., F. S. Leijten, K. G. Moons, E. P. Veltman, C. H. Ferrier, C. A. van Donselaar, M. Aramideh, C. J. Vecht, L. Wagner, E. Strijks, G. Hageman, R. S. Holscher, and D. Oenema. Epilepsy surgery can help many more adult patients with intractable seizures. Epilepsy Res. 101:210–216, 2012.

    Article  Google Scholar 

  26. Wiebe, S., D. R. Bellhouse, C. Fallahay, and M. Eliasziw. Burden of epilepsy: the Ontario Health Survey. Can. J. Neurol. Sci. 26:263–270, 1999.

    Article  CAS  Google Scholar 

  27. Willie, J. T., N. G. Laxpati, D. L. Drane, A. Gowda, C. Appin, C. Hao, D. J. Brat, S. L. Helmers, A. Saindane, S. G. Nour, and R. E. Gross. Real-time magnetic resonance-guided stereotactic laser amygdalohippocampotomy for mesial temporal lobe epilepsy. Neurosurgery 74:569–584, 2014.

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by the National Institutes of Health (NIH) under R01 NS120518. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Funding

This work was supported in part by the National Institutes of Health (NIH) under R01 NS120518. Vanderbilt University has filed patents related to the technology described in this paper. The most relevant is US Patent Number 10321963.

Conflict of interest

There are no financial interests or potential conflict of interest related to this work to disclose.

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Correspondence to R. J. Webster III.

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Associate Editor Ka-Wai Kwok oversaw the review of this article.

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Granna, J., Pitt, E.B., McKay, M.E. et al. Targeting Epilepsy Through the Foremen Ovale: How Many Helical Needles are Needed?. Ann Biomed Eng 50, 499–506 (2022). https://doi.org/10.1007/s10439-022-02929-w

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  • DOI: https://doi.org/10.1007/s10439-022-02929-w

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