RESUMEN
The mechanical properties of normal soft tissues, including breast tissue, have been of interest to the biomedical research community as there are many clinical and industrial applications that can benefit from quantitative information characterizing such properties. For instance, computer assisted surgery planning, elastography for breast cancer diagnosis, and bra design can all involve biomechanical modeling of the breast to predict its deformation or stress distribution. It is known that most biological soft tissues, including breast tissue, exhibit nonlinear mechanical response over large strains. As such, it is necessary to model such tissues as hyperelastic. In this work, we used indentation testing to estimate the hyperelastic parameters of 4 models (3rd order Ogden, 5-term polynomial, Veronda-Westman and Yeoh) estimated from 72 healthy ex vivo breast tissue samples covering adipose, fibroglandular, and mixed tissue. All estimated parameter sets were confirmed to represent stable material using Drucker's stability criterion. We observed that all three tissue types were statistically similar solidifying the use of homogenous breast modelling over large strain simulation.
Asunto(s)
Mama , Diagnóstico por Imagen de Elasticidad , Algoritmos , Fenómenos Biomecánicos , Simulación por Computador , Elasticidad , Análisis de Elementos Finitos , Humanos , Modelos Biológicos , Estrés MecánicoRESUMEN
The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community. There are several biomedical applications where parameters characterizing such properties are critical for a reliable clinical outcome. These applications include surgery planning, needle biopsy and brachtherapy where tissue biomechanical modeling is involved. Another important application is interpreting nonlinear elastography images. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize the nonlinear elasticity of tissues included in tissue slice specimens. This work presents hyperelastic measurement results of 44 pathological ex vivo breast tissue samples. For each sample, five hyperelastic models have been used, including the Yeoh, N = 2 polynomial, N = 1 Ogden, Arruda-Boyce, and Veronda-Westmann models. Results show that the Yeoh, polynomial and Ogden models are the most accurate in terms of fitting experimental data. The results indicate that almost all of the parameters corresponding to the pathological tissues are between two times to over two orders of magnitude larger than those of normal tissues, with C(11) showing the most significant difference. Furthermore, statistical analysis indicates that C(02) of the Yeoh model, and C(11) and C(20) of the polynomial model have very good potential for cancer classification as they show statistically significant differences for various cancer types, especially for invasive lobular carcinoma. In addition to the potential for use in cancer classification, the presented data are very important for applications such as surgery planning and virtual reality based clinician training systems where accurate nonlinear tissue response modeling is required.
Asunto(s)
Mama/patología , Elasticidad , Algoritmos , Diagnóstico por Imagen de Elasticidad , Humanos , Modelos Biológicos , Dinámicas no LinealesRESUMEN
The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community as there are several applications where parameters characterizing these properties are critical for a reliable outcome. This includes applications such as surgery planning, needle biopsy and cancer diagnosis using medical imaging. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize nonlinear elasticity of tissues included in slice specimens. In this paper, we present a method of measuring the hyperelastic parameters of tissue slice samples with tumours. In this method, to measure the hyperelastic properties of a tumour within a slice sample, the tumour was indented to acquire its force-displacement response while the slice remained intact. To calculate the hyperelastic parameters from the acquired data, we developed two inversion techniques that use the slice nonlinear finite element model as their forward problem solver. One of these techniques was based on nonlinear optimization while the other is a novel iterative technique that processes the variable slopes of the force-displacement response to calculate the hyperelastic parameters. The latter was developed specifically for the Yeoh and the second-order polynomial hyperelastic models, since we found that the other optimization-based inversion technique did not perform well with these models. To validate the proposed techniques, we performed numerical and phantom experiments. While we were able to achieve convergence with wide ranges of parameters of initial guesses to within 1% error with the numerical simulation experiments, we achieved convergence to within errors of around 5% with the tissue mimicking phantoms. Moreover, we successfully applied these techniques to data we acquired from nine pathological breast tissue slice specimens where the goal was to determine the hyperelastic properties of the tumour within the breast tissue slices.