An Improved Approach to Estimate Soft Tissue Parameters Using Genetic Algorithm for Minimally Invasive Measurement

This paper evaluates the ability of a gradient-free estimation method using genetic algorithm (GA) to model the elastic stress response of the anterior cruciate ligament (ACL) based on quasi-linear viscoelastic (QLV) theory. The improved GA simultaneously fits the ramping and relaxation experimental data to the QLV constitutive equation to obtain the soft tissue parameters. This approach is then compared with a previously evaluated method for two exponential and polynomial QLV models. The earlier approaches are mainly based on regression algorithms, which usually try to find a gradient-based solution with probability of poor convergence and variability of constants. Contrarily, this paper presents a gradient-free algorithm based on the improved timesaving GA. The results demonstrate that the ability of this algorithm to estimate the QLV parameters in timesaving process is functional to develop the optimal methodology for minimally invasive measurement during surgery.

[1]  Paolo P. Provenzano,et al.  Nonlinear Ligament Viscoelasticity , 2001, Annals of Biomedical Engineering.

[2]  Madan M. Gupta,et al.  SOFT COMPUTING AND INTELLIGENT SYSTEMS:THEORY AND APPLICATIONS , 2008 .

[3]  D P Pioletti,et al.  On the independence of time and strain effects in the stress relaxation of ligaments and tendons. , 2000, Journal of biomechanics.

[4]  D P Pioletti,et al.  Viscoelastic constitutive law in large deformations: application to human knee ligaments and tendons. , 1998, Journal of biomechanics.

[5]  Bijan Shirinzadeh,et al.  Soft Tissue Deformation with Neural Dynamics for Surgery Simulation , 2007, Int. J. Robotics Autom..

[6]  Cagatay Basdogan,et al.  Haptics in minimally invasive surgical simulation and training , 2004, IEEE Computer Graphics and Applications.

[7]  Savio L Woo,et al.  An improved method to analyze the stress relaxation of ligaments following a finite ramp time based on the quasi-linear viscoelastic theory. , 2004, Journal of biomechanical engineering.

[8]  Stefan Wesarg,et al.  VR-Based Simulators for Training in Minimally Invasive Surgery , 2007, IEEE Computer Graphics and Applications.

[9]  Gerhard A. Holzapfel,et al.  Nonlinear Solid Mechanics: A Continuum Approach for Engineering Science , 2000 .

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

[11]  Sivabal Sivaloganatha,et al.  Estimation of the quasi-linear viscoelastic parameters using a genetic algorithm , 2008, Math. Comput. Model..

[12]  Bijan Shirinzadeh,et al.  A Cellular Neural Network Methodology for Deformable Object Simulation , 2006, IEEE Transactions on Information Technology in Biomedicine.

[13]  T. Gill,et al.  The measurement of the variation in the surface strains of Achilles tendon grafts using imaging techniques. , 2006, Journal of biomechanics.

[14]  G. Li,et al.  The prediction of stress-relaxation of ligaments and tendons using the quasi-linear viscoelastic model , 2007, Biomechanics and modeling in mechanobiology.