Extracorporeal membrane oxygenation (ECMO) is increasingly deployed to provide percutaneous mechanical circulatory support despite incomplete understanding of its complex interactions with the failing heart and its effects on hemodynamics and perfusion. Using an idealized geometry of the aorta and its major branches and a peripherally inserted return cannula terminating in the iliac artery, computational fluid dynamic simulations were performed to (1) quantify perfusion as function of relative ECMO flow and (2) describe the watershed region produced by the collision of antegrade flow from the heart and retrograde ECMO flow. To simulate varying degrees of cardiac failure, ECMO flow as a fraction of systemic perfusion was evaluated at 100%, 90%, 75%, and 50% of total flow with the remainder supplied by the heart calculated from a patient-derived flow waveform. Dynamic boundary conditions were generated with a three-element lumped parameter model to accurately simulate distal perfusion. In profound failure (ECMO providing 90% or more of flow), the watershed region was positioned in the aortic arch with minimal pulsatility observed in the flow to the visceral organs. Modest increases in cardiac flow advanced the watershed region into the thoracic aorta with arch perfusion entirely supplied by the heart.

The increasing prevalence of finite element (FE) simulations in the study of atherosclerosis has spawned numerous inverse FE methods for the mechanical characterization of diseased tissue in vivo. Current approaches are however limited to either homogenized or simplified material representations. This paper presents a novel method to account for tissue heterogeneity and material nonlinearity in the recovery of constitutive behavior using imaging data acquired at differing intravascular pressures by incorporating interfaces between various intra-plaque tissue types into the objective function definition. Method verification was performed in silico by recovering assigned material parameters from a pair of vessel geometries: one derived from coronary optical coherence tomography (OCT); one generated from in silico-based simulation. In repeated tests, the method consistently recovered 4 linear elastic (0.1 ± 0.1% error) and 8 nonlinear hyperelastic (3.3 ± 3.0% error) material parameters. Method robustness was also highlighted in noise sensitivity analysis, where linear elastic parameters were recovered with average errors of 1.3 ± 1.6% and 8.3 ± 10.5%, at 5% and 20% noise, respectively. Reproducibility was substantiated through the recovery of 9 material parameters in two more models, with mean errors of 3.0 ± 4.7%. The results highlight the potential of this new approach, enabling high-fidelity material parameter recovery for use in complex cardiovascular computational studies.

The pathophysiology of atherosclerotic lesions, including plaque rupture triggered by mechanical failure of the vessel wall, depends directly on the plaque morphology-modulated mechanical response. The complex interplay between lesion morphology and structural behaviour can be studied with high-fidelity computational modelling. However, construction of three-dimensional (3D) and heterogeneous models is challenging, with most previous work focusing on two-dimensional geometries or on single-material lesion compositions. Addressing these limitations, we here present a semi-automatic computational platform, leveraging clinical optical coherence tomography images to effectively reconstruct a 3D patient-specific multi-material model of atherosclerotic plaques, for which the mechanical response is obtained by structural finite-element simulations. To demonstrate the importance of including multi-material plaque components when recovering the mechanical response, a computational case study was conducted in which systematic variation of the intraplaque lipid and calcium was performed. The study demonstrated that the inclusion of various tissue components greatly affected the lesion mechanical response, illustrating the importance of multi-material formulations. This platform accordingly provides a viable foundation for studying how plaque micro-morphology affects plaque mechanical response, allowing for patient-specific assessments and extension into clinically relevant patient cohorts.