Methodology for Haptic Modeling of Trocar Insertion Procedure

Trocar insertion is the first step in laparoscopic surgery procedures. It is a difficult procedure to learn and practice because it is carried out almost entirely without any visual feedback of the organs underlying the tissues being punctured. A majority of injuries are attributed to excessive use of force by surgeons. So there is a need for a haptic based computer simulator to train and improve the trocar insertion skills. In this paper, a new methodology for the modeling of trocar insertion is proposed. First, trocar insertion data (force/torque, displacement, etc.) are collected from animal models. Based on this data, a material model is computed using a hyper-elastic finite element computation (FEM). Using the FEM model, tissue deformation of the abdominal wall is calculated off-line for various conditions of tissue puncture. Deformation data are used to train a neural network which is, in turn, used to compute a real time virtual trocar insertion simulation. Force feedback is also modeled based on clinical data and is integrated into the simulator. This novel method allows for precise trocar insertion simulation based on prior FEM offline computation. The proposed system was implemented in a laboratory environment.Copyright © 2013 by ASME

[1]  Suvranu De,et al.  PhyNeSS: A Physics-driven Neural Networks-based Surgery Simulation system with force feedback , 2009, World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[2]  Miguel Srougi,et al.  Safety profile of trocar and insufflation needle access systems in laparoscopic surgery. , 2009, Journal of the American College of Surgeons.

[3]  Daljit Singh Sahota,et al.  Measurement of trocar insertion force using a piezoelectric transducer. , 2003, The Journal of the American Association of Gynecologic Laparoscopists.

[4]  Ridha Hambli,et al.  Real-time deformation of structure using finite element and neural networks in virtual reality applications , 2006 .

[5]  Gábor Székely,et al.  Data-Driven Haptic Rendering of Visco-Elastic Effects , 2008, 2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[6]  Venkat Krovi,et al.  Data Driven Development of Haptic Models for Needle Biopsy Phantoms , 2012 .

[7]  Bijan Shirinzadeh,et al.  Haptic deformation modelling through cellular neural network , 2006 .

[8]  Igor V. Tetko,et al.  Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture , 1998, J. Chem. Inf. Comput. Sci..

[9]  Karol Miller,et al.  Real-Time Nonlinear Finite Element Computations on GPU - Application to Neurosurgical Simulation. , 2010, Computer methods in applied mechanics and engineering.

[10]  Heather Carnahan,et al.  Trocar Insertion: The Neglected Task of VR Simulation , 2008, MMVR.

[11]  Hideo Fujimoto,et al.  Measurement of inserting motion of bladeless trocar at real surgery for development of a virtual training system for initial trocar placement in laparoscopic surgery. , 2011, Hepato-gastroenterology.

[12]  Christian Igel,et al.  Improving the Rprop Learning Algorithm , 2000 .

[13]  James F. O'Brien,et al.  Interactive simulation of surgical needle insertion and steering , 2009, SIGGRAPH 2009.

[14]  Thenkurussi Kesavadas,et al.  Data Acquisition and Development of a Trocar Insertion Simulator Using Synthetic Tissue Models , 2007, MMVR.

[15]  Sébastien Ourselin,et al.  Real-Time Nonlinear Finite Element Analysis for Surgical Simulation Using Graphics Processing Units , 2007, MICCAI.