Development of In Vivo Constitutive Models for Liver: Application to Surgical Simulation

Advancements in real-time surgical simulation techniques have provided the ability to utilize more complex nonlinear constitutive models for biological tissues which result in increased haptic and graphic accuracy. When developing such a model, verification is necessary to determine the accuracy of the force response as well as the magnitude of tissue deformation for tool–tissue interactions. In this study, we present an experimental device which provides the ability to obtain force–displacement information as well as surface deformation of porcine liver for in vivo probing tasks. In addition, the system is capable of accurately determining the geometry of the liver specimen. These combined attributes provide the context required to simulate the experiment with accurate boundary conditions, whereby the only variable in the analysis is the material properties of the liver specimen. During the simulation, effects of settling due to gravity have been taken into account by a technique which incorporates the proper internal stress conditions in the model without altering the geometry. Initially, an Ogden model developed from ex vivo tension and compression experimentation is run through the simulation to determine the efficacy of utilizing an ex vivo model for simulation of in vivo probing tasks on porcine liver. Subsequently, a method for improving upon the ex vivo model was developed using different hyperelastic models such that increased accuracy could be achieved for the force characteristics compared to the displacement characteristics, since changes in the force variation would be more perceptible to a user in the simulation environment, while maintaining a high correlation with the surface displacement data. Furthermore, this study also presents the probing simulation which includes the capsule surrounding the liver.

[1]  Jess Gerrit Snedeker,et al.  Mechanical Characterization of the Liver Capsule and Parenchyma , 2006, ISBMS.

[2]  Todd E. Zickler,et al.  Constitutive modeling of porcine liver in indentation using 3D ultrasound imaging. , 2009, Journal of the mechanical behavior of biomedical materials.

[3]  J G Snedeker,et al.  Strain-rate dependent material properties of the porcine and human kidney capsule. , 2005, Journal of biomechanics.

[4]  Cagatay Basdogan,et al.  Real-time visio-haptic interaction with static soft tissue models having geometric and material nonlinearity , 2010, Comput. Graph..

[5]  Jaydev P. Desai,et al.  Instrumentation for Testing Soft Tissue Undergoing Large Deformation: Ex Vivo and In Vivo Studies , 2008 .

[6]  Allison M. Okamura,et al.  Modeling of Tool-Tissue Interactions for Computer-Based Surgical Simulation: A Literature Review , 2008, PRESENCE: Teleoperators and Virtual Environments.

[7]  Jung Kim,et al.  Measurement and characterization of soft tissue behavior with surface deformation and force response under large deformations , 2010, Medical Image Anal..

[8]  Blake Hannaford,et al.  Biomechanical properties of abdominal organs in vivo and postmortem under compression loads. , 2008, Journal of biomechanical engineering.

[9]  K Miller,et al.  Mechanical properties of brain tissue in-vivo: experiment and computer simulation. , 2000, Journal of biomechanics.

[10]  Frank Tendick,et al.  Adaptive Nonlinear Finite Elements for Deformable Body Simulation Using Dynamic Progressive Meshes , 2001, Comput. Graph. Forum.

[11]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Amy E. Kerdok,et al.  Effects of perfusion on the viscoelastic characteristics of liver. , 2006, Journal of biomechanics.

[13]  Peter J. Hunter,et al.  A virtual environment and model of the eye for surgical simulation , 1994, SIGGRAPH.

[14]  Gábor Székely,et al.  Inverse Finite Element Characterization of Soft Tissues , 2001, MICCAI.

[15]  Hervé Delingette,et al.  Non-linear anisotropic elasticity for real-time surgery simulation , 2003, Graph. Model..

[16]  Karol Miller,et al.  Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation , 2009, Medical Image Anal..

[17]  Grigore C. Burdea,et al.  Force and Touch Feedback for Virtual Reality , 1996 .

[18]  Jaydev P. Desai,et al.  A 3D in-vivo constitutive model for porcine liver: Matching force characteristics and surface deformations , 2010, 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[19]  Peter Niederer,et al.  Virtual Reality-Based Simulation of Endoscopic Surgery , 2000, Presence: Teleoperators & Virtual Environments.

[20]  Matthew A. Reilly,et al.  A dynamic microindentation device with electrical contact detection. , 2009, The Review of scientific instruments.

[21]  Alessandro Nava,et al.  In vivo mechanical characterization of human liver , 2008, Medical Image Anal..

[22]  Zeike A. Taylor,et al.  Subject-specific non-linear biomechanical model of needle insertion into brain , 2008 .

[23]  G Székely,et al.  Virtual reality based surgery simulation for endoscopic gynaecology. , 1999, Studies in health technology and informatics.

[24]  Michael Bajka,et al.  The mechanical response of human liver and its relation to histology: An in vivo study , 2007, Medical Image Anal..

[25]  Cagatay Basdogan,et al.  A robotic indenter for minimally invasive measurement and characterization of soft tissue response , 2007, Medical Image Anal..

[26]  Jaydev P. Desai,et al.  Modeling Soft-Tissue Deformation Prior to Cutting for Surgical Simulation: Finite Element Analysis and Study of Cutting Parameters , 2007, IEEE Transactions on Biomedical Engineering.

[27]  Alfred Cuschieri,et al.  Measurements and modelling of the compliance of human and porcine organs , 2001, Medical Image Anal..

[28]  Blake Hannaford,et al.  In-Vivo and Postmortem Compressive Properties of Porcine Abdominal Organs , 2003, MICCAI.

[29]  J. Desai,et al.  Constitutive Modeling of Liver Tissue: Experiment and Theory , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[30]  Jaydev P. Desai,et al.  Estimating zero-strain states of very soft tissue under gravity loading using digital image correlation , 2010, Medical Image Anal..