Monday  |  Conference Center A  |  10:20 AM–10:40 AM

#13855, In-Silico Simulation of the Mechanical Response of an Atherosclerotic Cerebral Aneurysm in the Presence of Both Micro and Macro-Calcifications

Calcifications are linked to adverse cardiovascular events such as the rupture of fibrous caps in arterial stenosis and the failure of aneurysms. Calcified particles can significantly modulate the mechanical stability of tissues by altering the stress field in the adjacent extracellular matrix. In-silico studies have shown these effects can either be stabilizing or destabilizing depending on calcification size. Studies focused on macro calcifications suggest calcifications can play a protective role in fibrous cap stability [1]. In contrast, micro calcifications are postulated to have destabilizing effects by generating stress concentrations in the surrounding matrix [2]. However, none of these studies have investigated the combined effects of micro and macro calcifications on the mechanical response of such tissues. The characteristics of the surrounding matrix have also been shown to have a significant effect on tissue failure [3]. In particular, aneurysm samples with calcification embedded in lipid pools were less prone to rupture. Consistent with this finding, a recent computational study showed that lipid pools can attenuate the stress concentrations in the fibrous matrices around macro-calcifications [4]. However, an idealized lipid pool geometry was used in this study, due to technical challenges. Recently we developed a machine learning-based algorithm that overcame these challenges, enabling segmentation and 3D reconstruction of patient-specific geometries of lipid pools from micro–CT images.
This main objective of the current study is to use this tool to investigate the mechanical role of calcifications on the failure properties in patient specific atherosclerotic cerebral aneurysm tissues. Specimens are chosen with both micro and macro calcifications. Physiologically realistic geometries for the tissue, its calcifications, and lipid pool content are reconstructed using the machine learning-based algorithm [5]. Parametric studies are run to investigate the effect of lipid pools on the mechanical environment of these specimens. Studies of this kind are necessary to update risk assessment and avoid the medical risks associated unnecessary treatments.
1. Holzapfel, GA, et al. J Biomech . 2014.
2. Kelly-Arnold, A, et al. PNAS. 2013.
3. Gade, PS et al., ATVB, 2019
4. Fortunato, RN et al., Exp. Mech., 2021.
5. Ramezanpour, M, et al. Quantitative classification of calcifications in diseased tissues. Bonn, Germany, 2021.

Mehdi Ramezanpour   University of Pittsburgh
Yasutaka Tobe   University of Pittsburgh
Juan Cebral   George Mason University
Spandan Maiti   University of Pittsburgh
Anne Robertson   University of Pittsburgh