Modelling Intracranial Aneurysm Stability and Growth: An Integrative Mechanobiological Framework for Clinical Cases

Frederico S. Teixeira, Esra Neufeld, Niels Kuster, and Paul N. Watton, Biomechanics and Modeling in Mechanobiology 2020, online 12 June 2020; doi: 10.1007/s10237-020-01351-2

We present a novel patient-specific fluid-solid-growth framework to model the mechanobiological state of clinically detected intracranial aneurysms (IAs) and their evolution. The artery and IA sac are modeled as thick-walled, non-linear elastic fiber-reinforced composites. The undulation distribution of collagen fibers was represented as follows: the adventitia of the healthy artery is modeled as a protective sheath whereas the aneurysm sac is modeled to bear load within physiological range of pressures. Initially, we assume the detected IA is stable and then consider two flow-related mechanisms to drive enlargement: (1) low wall shear stress; and (2) dysfunctional endothelium which is associated with regions of high oscillatory flow. Localized collagen degradation and remodelling gives rise to formation of secondary blebs on the aneurysm dome. The restabilization of blebs is achieved by remodelling of the homeostatic collagen fiber stretch distribution. This integrative mechanobiological modelling workflow provides a step towards a personalized risk-assessment and treatment of clinically detected IAs.

The scientific and technical impact of the study can be summarized as:

  • A state-of-the-art structural model of clinically detected IAs that accounts for the probabilistic distribution of collagen fibers is introduced
  • Novel algorithms to characterize the heterogeneous physiological state of the healthy and diseased aneurysm wall and to determine the initial homeostatic and stabilized states have been developed
  • A computational framework focused on the generation of testable hypotheses has been successfully implemented, in which predictions can be correlated with experimental data from patient-specific evolution cases. On that basis, more predictive rupture risk indices can be formulated to guide clinical decision making