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

Abstract

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.

Calibration of Material Models for the Human Cervical Spine Ligament Behaviour Using a Genetic Algorithm

“…Genetic algorithm can be defined as a heuristic search method with the main purpose of optimization of the described system [39], based on the principles of natural evolution. Thus the genetic operators in numerical procedure are inspired by the natural evolution principles, such as selection, reproduction, mutation and crossover [11,19,23,39]. Together with the definition of its main properties, such as population initialization, definition of results domains, convergence rule and generations progress rule it is a powerful tool for the calibration of material models of nonlinear systems, which are based on experimental data gained from the materials response to the probing signal.…”

“…The first step in the definition of genetic algorithm should be the creation of the initial population. It is suggested that one or more individuals should be selected heuristically, instead of doing it all stochastically [23].…”

“…Larger population will have more solutions included, but would slow down the process of computing, so the user should find the optimal value. Madjidi et al used both n=100 and n=200 individuals, with both populations producing good results [23]. In [7], only the population of n=100 was used.…”

“…The most influential variable in the mutation process is mutation ratio. Madjidi et al suggested a mutation ratio of 0.01 [23], Louchet et al suggested 0.2 [14] and Wright suggested between 0.005 and 0.3 [10], depending on the algorithm. Bianchi and Solenthaler also suggested adaptive mutation, but with a different mutation ratio function [24].…”

“…This has provided quicker and easier diagnostics and improved the healthcare systems [20]. Examples for the use of the genetic algorithms in tissue mechanics and/or parameter determination include: breast tissues [21,22], anterior cruciate ligament [23], mass-spring models for soft tissues [24], QLV theory [7,23], ventricular myocardium [25], liver [26], porcine coronary media and adventitia [27], sheep tendons [28] and human placenta [29].…”