An improved panoramic digital image correlation method for vascular strain analysis and material characterization

The full potential of computational models of arterial wall mechanics has yet to be realized primarily because of a lack of data sufficient to quantify regional mechanical properties, especially in genetic, pharmacological, and surgical mouse models that can provide significant new information on the time course of adaptive or maladaptive changes as well as disease progression. The goal of this work is twofold: first, to present modifications to a recently developed panoramic-digital image correlation (p-DIC) system that significantly increase the rate of data acquisition, overall accuracy in specimen reconstruction, and thus full-field strain analysis, and the axial measurement domain for in vitro mechanical tests on excised mouse arteries and, second, to present a new method of data analysis that similarly increases the accuracy in image reconstruction while reducing the associated computational time. The utility of these advances is illustrated by presenting the first full-field strain measurements at multiple distending pressures and axial elongations for a suprarenal mouse aorta before and after exposure to elastase. Such data promise to enable improved inverse characterization of regional material properties using established computational methods.