Tissue stiffness from tactile imaging

Abstract

Tactile imaging uses a passive pressure sensitive sensor to record the reaction pressure distribution on its surface. When pressed into and scanned over tissue of interest, it generates an image that is dependent on the mechanical properties and geometric distribution of the structures within the tissue. Since pathology is related to tissue stiffness, stiffness measurement would be of great use to provide insight into disease processes and an aid to diagnosis. We have developed an algorithm that inverts the tactile image and determines the salient features of the underlying tissue. The features of interest are the stiffness and depth of the background tissue and the stiffness and size of a round inclusion. A finite element model was constructed in order to simulate the tactile imaging process. The analysis was performed on models spanning an experimentally determined range of material properties. By analyzing each pressure frame and performing a least-squares fit between the pressure frames and the model features we were able to determine an inverse relationship to extract tissue stiffness from tactile frames.