New Approaches to Catheter Navigation for Interventional Radiology Simulation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3750)


For over 20 years, interventional methods have improved the outcomes of patients with cardiovascular disease. However, these procedures require an intricate combination of visual and tactile feedback and extensive training periods. In this paper, we describe a series of novel approaches that have lead to the development of a high-fidelity simulation system for interventional neuroradiology. In particular we focus on a new approach for real-time deformation of devices such as catheters and guidewires during navigation inside complex vascular networks. This approach combines a real-time incremental Finite Element Model, an optimization strategy based on substructure decomposition, and a new method for handling collision response in situations where the number of contacts points is very large. We also briefly describe other aspects of the simulation system, from patient-specific segmentation to the simulation of contrast agent propagation and fast volume rendering techniques for generating synthetic X-ray images in real-time.


Interventional Neuroradiology Local Compliance Contact Response Catheter Navigation Multibody Dynamic Model 
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  1. 1.
    Alderliesten, T.: Simulation of Minimally-Invasive Vascular Interventions for Training Purposes. PhD dissertation, Utrecht University (December 2004)Google Scholar
  2. 2.
    American Heart Association. Heart and stroke facts statistics: Statistical supplement. American Heart Association, Dallas, Texas (1999)Google Scholar
  3. 3.
    Cotin, S., Dawson, S., Meglan, D., Shaffer, D., Ferrell, M., Sherman, P.: Icts, an interventional cardiology training system. In: Westwood, J.D., et al. (eds.) Proceedings of Medicine Meets Virtual Reality, pp. 59–65. IOS Press, Amsterdam (2000)Google Scholar
  4. 4.
    Duriez, C., Andriot, C., Kheddar, A.: Signorini’s contact model for deformable objects in haptic simulations. In: IEEE-IROS, pp. 3232–3237 (2004)Google Scholar
  5. 5.
    Featherstone, R.: The calculation of robot dynamics using articulated-body inertias. International Journal of Robotics Research 2(1), 13–30 (1983)CrossRefGoogle Scholar
  6. 6.
    Hoefer, U., Langen, T., Nziki, J., Zeitler, F., Hesser, J., Mueller, U., Voelker, W., Maenner, R.: Cathi - catheter instruction system. In: Computer Assisted Radiology and Surgery (CARS), Paris, France, pp. 101–106 (2002)Google Scholar
  7. 7.
    Jourdan, F., Alart, P., Jean, M.: A gauss-seidel like algorithm to solve frictional contact problems. Comp. Meth. in Appl. Mech. and Eng., 33–47 (1998)Google Scholar
  8. 8.
    Lenoir, J., Meseure, P., Grisoni, L., Chaillou, C.: Surgical thread simulation. In: MS4CMS (Proc. of ESAIM), vol. 12, pp. 102–107 (2002)Google Scholar
  9. 9.
    Nowinski, W.L., Chui, C.K.: Simulation of interventional neuroradiology procedures. In: MIAR, pp. 87–94 (2001)Google Scholar
  10. 10.
    Przemieniecki, J.S.: Theory of Matrix Structural Analysis (1968)Google Scholar
  11. 11.
    Wu, X., Pegoraro, V., Lubos, V., Neumann, P., Bardsley, R., Dawson, S., Cotin, S.: New approaches to computer-based interventional neuroradiology training. In: Westwood, J.D., et al. (eds.) Proceedings of MMVR, pp. 602–607 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Sim Group, CIMITCambridge
  2. 2.Harvard Medical SchoolBostonUSA

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