An Experimental Validation of Behavior-Based Motions for Robotic Coronary Guidewire Crossing Techniques

Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 11)


Percutaneous Coronary Intervention (PCI) is a non-surgical approach used to open narrowed coronary arteries and restore arterial blood flow to heart tissue. During a PCI procedure, the clinician uses X-ray fluoroscopy to visualize and guide the catheter, guidewire, and other devices (e.g. angioplasty balloons and stents). Robot-assisted PCI could potentially reduce the radiation exposure of operators and provide a more ergonomic workflow. However, modeling and controlling of the guidewire remains challenging because of the interplay of guidewire motions, the tip properties (e.g., loads, coating), and the local cross-sectional area of the vessel lumen (e.g, stenosis) and results in a highly non-linear system. Thus, robot-assisted PCI devices are still passively controlled by human operators at the cockpit. In this paper, we introduce methods to generate distal guidewire motions that take advantage of the fast response of a robotic system and which may be difficult to generate by a human hand. The fundamental motions that a robot can use to control the movement and direction of the guidewire are rotation and pushing/retracting, from the proximal end of the guidewire outside the insertion point on the patient’s body. We begin by investigating combinations of these fundamental motions under structured environmental settings and conduct a systematic empirical comparison of task completion time for a given setting. We then demonstrate improved dynamic behavior motions for a soft guidewire, which shows a promising speed-up by \(33\%\) and \(44\%\) for two difficult stenosis cases.


Robot-assisted PCI Guidewire navigation Behavior-based control High-speed dynamic motion Medical robots 



This feature is based on research, and is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.


  1. 1.
    Kini, A., Sharma, S., Narula, J.: Practical Manual of Interventional Cardiology. Springer, New York (2014)CrossRefGoogle Scholar
  2. 2.
    Maor, E., Eleid, M.F., Gulati, R., Lerman, A., Sandhu, G.S.: Current and future use of robotic devices to perform percutaneous coronary interventions: a review. J. Am. Heart Assoc. 6(7), e006239 (2017)CrossRefGoogle Scholar
  3. 3.
    Schröder, J.: The mechanical properties of guidewires. Part I: stiffness and torsional strength. Cardiovasc. Intervent. Radiol. 16, 43–46 (1993)CrossRefGoogle Scholar
  4. 4.
    Liau, C.-S., Ho, S.-G., Chen, S.-N., Yang, L.-F.: A new guidewire technique for difficult cardiac catheterization. Cardiology 97, 24–28 (2001)CrossRefGoogle Scholar
  5. 5.
    Dash, D.: Guidewire crossing techniques in coronary chronic total occlusion intervention: A to Z. Indian Heart J. 68, 410–420 (2016)CrossRefGoogle Scholar
  6. 6.
    Konings, M.K., van de Kraats, E.B., Alderliesten, T., Niessen, W.J.: Analytical guide wire motion algorithm for simulation of endovascular interventions. Med. Biol. Eng. Comput. 41(6), 689–700 (2003)CrossRefGoogle Scholar
  7. 7.
    Luboz, V., Blazewski, R., Gould, D., Bello, F.: Real-time guidewire simulation in complex vascular models. Vis. Comput. 25(9), 827–834 (2009)CrossRefGoogle Scholar
  8. 8.
    Tang, W., Wan, T.R., Gould, D.A., How, T., John, N.W.: A stable and real-time nonlinear elastic approach to simulating guidewire and catheter insertions based on cosserat rod. IEEE Trans. Biomed. Eng. 59(8), 2211–2218 (2012)CrossRefGoogle Scholar
  9. 9.
    Wang, H., Wu, J., Wei, M., Ma, X.: A robust and fast approach to simulating the behavior of guidewire in vascular interventional radiology. Comput. Med. Imag. Graph. 40, 160–169 (2015)CrossRefGoogle Scholar
  10. 10.
    He, B., Xu, S., Ding, A., Zhou, Y.: Analysis of rotation angles and motions of the flexible mechanisms in bifurcated blood vessels. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 231(8), 747–757 (2017)CrossRefGoogle Scholar
  11. 11.
    Sharei, H., Alderliesten, T., van den Dobbelsteen, J.J., Dankelman, J.: Navigation of guidewires and catheters in the body during intervention procedures: a review of computer-based models. J. Med. Imag. 5(1), 010902 (2018)CrossRefGoogle Scholar
  12. 12.
    Shell, D.A., Matarić, M.J.: Behavior-based methods for modeling and structuring control of social robots. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, Chap. 11. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  13. 13.
    P.C. for Agile Semi-Autonomous Ground Vehicles using Motion Primitives, “Predictive Control for Agile Semi-Autonomous Ground Vehicles using Motion Primitives”. In: Proceedings of American Control Conference, Montréal, Canada, June 2012Google Scholar
  14. 14.
    Paranjape, A.A., Meier, K.C., Shi, X., Chung, S.-J., Hutchinson, S.: Motion primitives and 3D path planning for fast flight through a forest. Int. J. Robot. Res. 34(3), 357–377 (2015)CrossRefGoogle Scholar
  15. 15.
    Kim, Y.-H., Shell, D.A.: Using a compliant, unactuated tail to manipulate objects. IEEE Robot. Autom. Lett. 2(1), 223–230 (2017) CrossRefGoogle Scholar
  16. 16.
    Kim, Y.-H., Kapoor, A., Finocchi, R., Girard, E.: Evaluation of high-speed dynamic motions for robotic guidewire crossing techniques. In: Proceedings of Hamlyn Symposium on Medical Robotics, London, United Kingdom, June 2018Google Scholar
  17. 17.
    Madder, R., Lombardi, W., Parikh, M., Kandzari, D., Grantham, J., Rao, S.: Impact of a novel advanced robotic wiring algorithm on time to wire a coronary artery bifurcation in a porcine model. J. Am. Coll. Cardiol. 70(80) (2017)Google Scholar
  18. 18.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, New York (2006)CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Rafii-Tari, H., Liu, J., Payne, C.J., Bicknell, C., Yang, G.-Z.: Hierarchical HMM based learning of navigation primitives for cooperative robotic endovascular catheterization. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI), Boston, USA, September 2014Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Siemens Healthineers, Medical Imaging TechnologiesPrincetonUSA

Personalised recommendations