Mimicking of Cerebral Autoregulation by Flow-Dependent Cerebrovascular Resistance: A Feasibility Study

Understanding circulatory autoregulation is essential for improving physiological control of rotary blood pumps and support conditions during cardiopulmonary bypass (CPB). Cerebral autoregulation (CAR), arguably the most critical, is the body's intrinsic ability to maintain sufficient cerebral blood flow (CBF) despite changes in aortic perfusion pressure. It is therefore imperative to include this mechanism into computational fluid dynamics (CFD), particle image velocimetry (PIV), or mock circulation loop (MCL) studies. Without such inclusions, potential losses of CBF are overestimated. In this study, a mathematical model to mimic CAR is implemented in a MCL- and PIV-validated CFD model. A three-dimensional model of the human vascular system was created from magnetic resonance imaging records. Numerical flow simulations were performed for physiological conditions and CPB. The inlet flow was varied between 4.5 and 6 L/min. Arterial outlets were modeled using vessel-specific, flow-dependent cerebrovascular resistances (CVRs), resulting in a variation of the pressure drop between 0 and 80 mm Hg. CBF is highly dependent on the level of CAR during CPB. By varying the CVR parameters up to the beginning of plateau phase, it can be regulated between 0 and 80% of physiological CBF. So while implementing autoregulation, CBF remains unchanged during a simulated native cardiac output of 5 L/min or CPB support of 6 L/min. Neglecting CAR, constant backflow from the brain occurs for some cannula positions. Using flow-dependent CVR, CBF returns to its baseline at a rate of recovery of 0.25 s. Results demonstrate that modeling of CAR by flow-dependent CVR delivers feasible results. The presented method can be used to optimize physiological control of assist devices dependent upon different levels of CAR representing different patients.