Characterizing Tissue Remodeling and Mechanical Heterogeneity in Cerebral Aneurysms

Abstract

Accurately assessing the complex tissue mechanics of cerebral aneurysms (CAs) is critical for elucidating how CAs grow and whether that growth will lead to rupture. The factors that have been implicated in CA progression – blood flow dynamics, immune infiltration, and extracellular matrix remodeling – all occur heterogeneously throughout the CA. Thus, it stands to reason that the mechanical properties of CAs are also spatially heterogeneous. Here, we present a new method for characterizing the mechanical heterogeneity of human CAs using generalized anisotropic inverse mechanics, which uses biaxial stretching experiments and inverse analyses to determine the local Kelvin moduli and principal alignments within the tissue. Using this approach, we find that there is significant mechanical heterogeneity within a single acquired human CA. These results were confirmed using second harmonic generation imaging of the CA’s fiber architecture and a correlation was observed. This approach provides a single-step method for determining the complex heterogeneous mechanics of CAs, which has important implications for future identification of metrics that can improve accuracy in prediction risk of rupture.

It's more than the amount that counts: implications of collagen organization on passive muscle tissue properties revealed with micromechanical models and experiments

“…Thickness was measured in nine evenly spaced locations and averaged while the sample was unpinned in a stress-free state, outside of any solution. The tissue was then embedded in optimal cutting temperature compound and snap frozen in liquid nitrogen cooled isopentane for sectioning and imaging [27,28]. We assumed symmetry between the two halves of the diaphragm, as differences in microstructure are not observed between the left and right sides [12,29].…”

“…Images of the tissue sample were collected during the duration of the test and we used a previously established digital image correlation software [30] for strain tracking analysis. This method is capable of detecting regional changes in anisotropy and heterogeneity [30] and has been applied in soft, hydrated tissues [27,28]. Briefly, a region of interest within the rakes is selected from an initial image, meshed and mapped onto subsequent images.…”

Combining Unique Planar Biaxial Testing with Full‐Field Thickness and Displacement Measurement for Spatial Characterization of Soft Tissues

“…If desired, the data can be fitted using an inverse method to parameterize the sample's mechanical behavior according to a constitutive model or strain-energy function selected to accurately reflect the sample's behavior under the prescribed loading conditions. Parameterizations have been achieved using neo-Hookean, Mooney-Rivlin, Fung exponential, and Holzapfel-Gasser-Ogden strainenergy functions (Davis et al, 2015;Katia Genovese et al, 2014;Kroon & Holzapfel, 2008;Raghupathy & Barocas, 2010;Raghupathy, Witzenburg, Lake, Sander, & Barocas, 2011;Seshaiyer & Humphrey, 2003;Shih et al, 2021;Witzenburg et al, 2012;Zhao et al, 2009Zhao et al, , 2011.…”

“…A variety of inverse techniques have been implemented in direct, iterative, and pointwise manners to model soft tissue mechanics (Davis et al., 2015; Katia Genovese et al., 2014; Kroon & Holzapfel, 2008; Seshaiyer & Humphrey, 2003; Zhao et al., 2009, 2011). In the past, we have developed and applied a generalized anisotropic inverse mechanics method to estimate regional differences in stiffness and mechanical anisotropy (Raghupathy & Barocas, 2010; Raghupathy et al., 2011; Shih et al., 2021; Witzenburg et al., 2012), but the resultant data from this novel technique are of independent value, and their analysis need not be limited to our inverse method.…”

“…These three sets of data—full‐field displacement, enhanced boundary force information, and full‐field thickness—can be combined to yield detailed spatial descriptions of mechanics. Spatially varying material properties could then be fitted to a constitutive model via a number of successful inverse techniques (Davis, Luo, Avril, Duprey, & Lu, 2015; Katia Genovese, Casaletto, Humphrey, & Lu, 2014; Kroon & Holzapfel, 2008; Seshaiyer & Humphrey, 2003; Shih et al., 2021; Witzenburg, Raghupathy, Kren, Taylor, & Barocas, 2012; Zhao, Chen, & Lu, 2009, 2011).…”