A network-based approach to investigate intravascular flow coherence in personalized computational models of human carotid bifurcation

The blood flow patterns in carotid arteries are extremely complex, inspiring a lot of studies in the literature focused on the definition and application of different quantitative tools in order to describe the hemodynamics and to understand the risks associated with the onset/progression of vascular disease. This is because hemodynamics in the carotid arteries is characterized by a space-time heterogeneity due to the non-symmetrical nature of the carotid bifurcation, the presence of tortuosity and non-planar curvatures, and the compliance of the walls, which contribute to the development of anterograde and retrograde intravascular blood flow structures, with secondary flow components. The aim of this work is to delineate and interpret coherent large-scale flow structures in the carotid bifurcation by capturing the spatiotemporal evolution of correlated blood flow patterns and their anatomical length of persistence, thus contributing to the understanding of the organization patterns of hemodynamic flows. To do that, an integrated computational hemodynamics and Complex Networks-based approach has been applied on 31 personalized hemodynamic models of carotid bifurcation of healthy subjects, obtained from MRI acquisitions. Following previous studies, correlation-based “one-to-all” networks are built computing the pairwise correlation between the blood axial velocity waveform along the cardiac cycle, defined in each node of the computational grid, and the subject-specific inflow rate in the common carotid artery, the latter considered as one of the main determinants of intravascular hemodynamics. A network-based metric was computed to quantify the anatomical persistence length of the correlation between inlet flow rate and axial flow in the carotid bifurcation. Such quantity was then related to geometric parameters, such as the bifurcation expansion (or flare), previously found to be a surrogate marker of disturbed flow, and the vascular tortuosity, to flow parameters, and to the volume of recirculating flow inside the bifurcation. The main results highlighted an evident inter-variability in terms of the correlations between the driving flow rate waveform and the axial velocity waveforms in the investigated dataset. By increasing the volume of recirculating flow, the non-symmetrical nature of the vessel, here represented by the bifurcation flare, significantly interrupts the correlations between axial flow and inflow rate, reducing the effect of the latter on the large-scale dominant flow structures. Moreover, flow parameters (e.g., pulsatility and peak-to-peak amplitude), were associated with the recirculation volume in the bifurcation, but they did not have a significant impact on the relationship between axial flow and blood flow rate. In conclusion, the results of this work showed the efficiency of an approach based on the Theory of Complex Networks in characterizing highly complex and space-time heterogeneous coherent blood flow structures, quantifying for the first time the effect of the subject-specific flow rate on the spatiotemporal evolution of large-scale axial flow.