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.
A framework has been investigated to enable a variety of comparative studies in the context of needle-based gynaecological brachytherapy. Our aim was to create an anthropomorphic phantom-based platform. The three main elements of the platform are the organ model, needle guide, and needle drive. These have been studied and designed to replicate the close environment of brachytherapy treatment for cervical cancer. Key features were created with the help of collaborating interventional radio-oncologists and the observations made in the operating room. A phantom box, representing the uterus model, has been developed considering available surgical analogies and operational limitations, such as organs at risk. A modular phantom-based platform has been designed and prototyped with the capability of providing various boundary conditions for the target organ. By mimicking the female pelvic floor, this framework has been used to compare a variety of needle insertion techniques and configurations for cervical and uterine interventions. The results showed that the proposed methodology is useful for the investigation of quantifiable experiments in the intraabdominal and pelvic regions.
This paper presents a gradient-free direct search estimation method by using genetic algorithm to model and predict the elastic stress response of ligament based on quasi-linear viscoelastic (QLV) theory. An improved genetic algorithm is developed to simultaneously fit the ramping and relaxation experimental data to the QLV constitutive equation for obtaining soft tissue parameters in a time-saving process. Experiments and comparison analysis with the existing methods for two exponential and polynomial QLV models are conducted, demonstrating that the proposed method can accurately estimate soft tissue parameters and satisfy the time-saving requirement of intraoperative soft tissue characterization.
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