A multi-body mass-spring model for virtual reality training simulators based on a robotic guide wire operating system

Generally, surgeons of minimally invasive surgery should possess good ability to coordinate their both hands. The manipulation of guide wire is considered a core skill. Obtaining that core skill to perform minimally invasive surgery requires training. In minimally invasive surgery, a surgical robot can assist doctors to position precisely and provide stable operation platform. Therefore, to develop a virtual reality simulators for training purpose based on a robotic guide wire operating system is an important and challenging subject. In this paper, a multi-body mass-spring model for simulating guide wire is presented and evaluated. In order to overcome the disadvantage of using mass-spring approach to model the guide wire, we propose a new collision detection algorithm and a new collision response algorithm. Finally, we test our guide wire with a complex and realistic 3D vascular model, which is selected from computer tomography database of real patients. The result shows that the virtual reality training simulators is effective and promising.

[1]  Fernando Bello,et al.  A Virtual Environment for Core Skills Training in Vascular Interventional Radiology , 2008, ISBMS.

[2]  H. Bleuler,et al.  A Computer-Based Real-Time Simulation of Interventional Radiology , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Wiro J. Niessen,et al.  Modeling Friction, Intrinsic Curvature, and Rotation of Guide Wires for Simulation of Minimally Invasive Vascular Interventions , 2007, IEEE Transactions on Biomedical Engineering.

[4]  Andrew P. Witkin,et al.  Large steps in cloth simulation , 1998, SIGGRAPH.

[5]  Peter B. Noël,et al.  Real-time endovascular guidewire position simulation using shortest path algorithms , 2009, International Journal of Computer Assisted Radiology and Surgery.

[6]  Chee-Kong Chui,et al.  Simulation of interventional neuroradiology procedures , 2001, Proceedings International Workshop on Medical Imaging and Augmented Reality.

[7]  W. Niessen,et al.  Analytical guide wire motion algorithm for simulation of endovascular interventions , 2003, Medical and Biological Engineering and Computing.

[8]  Alejandro M. García-Alonso,et al.  Solving the collision detection problem , 1994, IEEE Computer Graphics and Applications.

[9]  Stephane Cotin,et al.  New approaches to computer-based interventional neuroradiology training. , 2005, Studies in health technology and informatics.

[10]  C. Duriez,et al.  New approaches to catheter navigation for interventional radiology simulation , 2006, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[11]  R. Featherstone.  The Calculation of Robot Dynamics Using Articulated-Body Inertias , 1983 .

[12]  Fernando Bello,et al.  Real-time guidewire simulation in complex vascular models , 2009, The Visual Computer.

[13]  Wiro J Niessen,et al.  Simulation of minimally invasive vascular interventions for training purposes† , 2004, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[14]  Chee-Kong Chui,et al.  Training and pretreatment planning of interventional neuroradiology procedures--initial clinical validation. , 2002, Studies in health technology and informatics.

[15]  Frode Laerum,et al.  The acquisition of skills in interventional radiology by supervised training on animal models: A three-year multicenter experience , 2004, CardioVascular and Interventional Radiology.