Computational hemodynamics & complex networks integrated platform to study intravascular flow in the carotid bifurcation

The well-established role of hemodynamics in cardiovascular disease [1] makes the study of cardiovascular flows of wide interest. Here we apply for the first time a method based on complex networks (CNs) theory [2] to investigate and characterize quantitatively the complexity of cardiovascular flows. The rationale lies in the ability of CNs to explore the complexity of physical systems, such as 4D cardiovascular flows, in a synthetic and effective manner. CN-based approaches have already proven useful for data-driven learning of dynamical processes that are hidden to other analysis techniques. In detail, a dataset of 10 patient-specific computational hemodynamics models of human carotid bifurcation (CB) is considered here. Quantitative metrics derived from CNs theory are applied to two fluid mechanics quantities describing the intricate intravascular hemodynamics. These are (1) the so-called axial velocity, i.e. the blood velocity component aligned with the main flow direction, as identified by the vessels centerline, and (2) the kinetic helicity density, a measure of pitch and torsion of the streaming blood. The obtained results suggest the potency of CNs in unveiling fundamental organization principles in cardiovascular flows.