Atherosclerotic plaque rupture is the underlying cause of 50% of deaths in western society. Although screening methodologies exist, plaques are highly complex, and the parameters used to measure plaque vulnerability are often insufficient for correct medical screening. Since plaque rupture occurs when the vascular forces exceed the plaque strength, the mechanical analysis of plaque stability could offer a new window into predicting rupture. By interpreting the mechanical behaviour of plaques, so-called mechanical markers could be derived, which could highlight vulnerable plaques before thrombosis. Unfortunately, the extensively modelled local stresses and energy functions are immeasurable in vivo. Therefore we turned to strains, a measure of material deformation that can be obtained clinically. This study investigates the predictive value of strain distributions in the early detection of fibrous caps rupture.
Simplistic plaque cap mimics were engineered to model atherosclerotic plaque rupture. Made from a fibrinous matrix and a soft lipidic inclusion (SI), the constructs were tailored to have mechanical properties similar to in vivo plaque caps. Tissue engineered caps offer a wide range of advantages over endarterectomy samples, including unlimited sample availability, robust geometry, high reproducibility and precise control over biological constituents. These constructs were uniaxially strained and subjected to 2D digital image correlation (DIC) to obtain their strain fields. Two-dimensional DIC is an algorithm that derives the material deformation by tracking surface features of its target sample. Therefore, it can very accurately calculate local strains for a material. This report analyses the patterns and maxima of the strain maps of five samples to evaluate unique features distinct at the rupture location that could serve as potential markers of plaque vulnerability. Besides inspecting the individual strain maps, their collective contribution through two strain-based failure criteria was analysed. These failure criteria approximate the strain energy in the samples.
The mechanical failure of the constructs was not instantaneous. All samples underwent failure in phases, starting with a small crack in the SI and concluding with the rupture of the fibrous tissue. Likewise, the strain and failure criteria patterns were consistent within all samples. Even though their strain values differ, the accumulations of low and high strains lie at nearly identical positions. These patterns emerged early in the tensile experiment, as the frames at a physiological (10%) and final (ultimate state before rupture) global strain measured a strong resemblance. In a more localised analysis, the fields were radially divided into ‘slices’ of data to produce distinct segment-based patterns. The rupture location consistently lies at a unique feature in the pattern, such as a peak or a valley. As was observed before, aside from the difference in their magnitude, the strain patterns showed no change between the frames. Finally, the local maxima of the strain and failure criteria maps were inspected. Their distances to the rupture site were comparable for all maps, indicating they performed similarly at estimating the rupture location. Moreover, the distances measured for the final frame showed no significant difference to those measured for the physiological frame.
The mechanical response of the five samples is similar. Aside from undergoing mechanical rupture in stages, the analysis of the tissue construct shows that a relationship exists between the local strains and rupture location. First, the rupture location always sits at the edge of a high valued region in the SI. Second, the tissue segmentation produces a highly reproducible pattern with a distinct colocalisation between segment patterns and the rupture location. Third, the maxima of the strain and failure criteria maps lie close to the rupture site, although they do not overlap. Accordingly, there is a clear relationship between the rupture location and the
local strain patterns.