Date of Award

2008

Degree Type

Thesis

Degree Name

Master of Engineering Science

Program

Electrical and Computer Engineering

Supervisor

Dr. Abbas Samani

Abstract

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 clinical 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 non-linear elasticity of tissues included in slice specimens. In this work a method for measuring the hyperelastic parameters of tissue slice samples with tumours is presented. 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, two inversion techniques were developed 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 ofthe force-displacement response to calculate the hyperelastic parameters. The latter was developed specifically for the Yeoh and the second order Polynomial hyperelastic model, since it was found that the other optimization based inversion technique did not perform well with these models. To validate the proposed techniques, numerical and phantom experiments were performed. Convergence with wide ranges of parameters of initial guesses was achieved, to within 1% error with the numerical simulation experiments, and also with errors of around 5-10% with the tissue mimicking phantoms. Moreover, these techniques were successfully applied to data that was acquired from 44 pathological breast tissue slice specimens where the goal was to determine the hyperelastic properties of the tumour within the breast tissue slices. A statistical analysis was performed in an attempt to correlate specific hyperelastic propertiestotissuepathology. Itwasconcludedthatfurtherresearchisrequiredto ascertain the reliability of using a hyperelastic parameter for cancer classification. It was also concluded that, based on the available data, it may be difficult to identify specific

pathologies based solely on individual hyperelastic parameters and that a consideration of the entire parameter set may be necessary and that factors other than tissue pathology may be involved in tissue stifftιess, such as age.

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