Shape-Based Strategy for Inverse Estimation of Soft Tissue Mechanical Material Properties from Untagged Medical Imaging Data

Abstract

A computational approach is presented to estimate the in vivo magnitude and spatial distribution of mechanical material properties of soft tissues. This strategy utilizes an optimization-type inverse problem solution procedure with a shape-based objective function to estimate the difference between the measured and predicted tissue behavior and estimate mechanical properties from standard untagged clinical imaging data. A set of simulated inverse problems was used to evaluate the inverse solution estimation procedure based on estimating the passive elasticity of the human right and left ventricle walls from standard cardiac imaging data and corresponding hemodynamic measurements. Results showed that the proposed shape-based approach can estimate mechanical material properties accurately, although the accuracy is dependent on the spatial region considered and the relative stiffness. The solution accuracy also was shown to be tolerant to the presence of model error. Moreover, additional tests showed that the accuracy of the material property estimate of a priority region of the structure of interest can be improved, while simultaneously desensitizing the solution to additional model error, by selecting an appropriate portion of the response shape to use within the objective function.