Mechanical Characterization of Tissue - like Materials Using Information Based Machine Learning

Changes in the mechanical properties of soft tissues may be indicative of disease processes. Medical elastography techniques are an attempt to create images of the mechanical behavior to increase the sensitivity and specificity of existing imaging modalities. Current quantitative elasticity imaging methods rely on a priori assumptions of the tissue biomechanics in order to simply the forward problem, from which an inverse problem is developed. Erroneous assumptions and noisy image data result in incorrect estimates of the mechanical parameters. This thesis presents a new method of characterizing the mechanical response of soft tissues. Machine-learning techniques and measured force-displacement data are used to create empirical models of the constitutive behavior. Informational models are developed without enforcing simplyfing assumptions of the true underlying mechanics, allowing the mechanical properties of the tissue to be investigated after the model is developed. Knowledge of the true behavior allows the appropriate consitutive model to be chosen to create a parametric summary of the tissue suitable for imaging. The informational modeling process is demonstrated on gelatin phantoms comprised of a soft background material with one or three stiffer inclusions. An ultrasound probe was used to uniaxially compress the phantoms while acquiring surface force and displacement data, as well as ultrasound images. A speckle-tracking algorithm estimated motion of the phantoms within the imaged region. Force-displacement data and the Autoprogressive training algorithm was then used to build informational models describing the constitutive behavior of the gelatin materials. It will be shown that estimates of the full stress and strain vectors throughout an entire