Cerebral Perfusion and Autoregulation

Realistic cerebrovascular simulations require boundary conditions that represent the blood-flow demand of the supplied brain tissue. We estimate subject-specific flow distributions by associating image-derived cerebral arteries with surrounding tissue regions. The required flow for each territory is determined from tissue mass and regional cerebral blood-flow demand and is distributed through a one-dimensional vascular model.

The model is evaluated by comparing estimated arterial flow distributions and perfusion territories with reported physiological data and perfusion imaging. The supply–demand formulation also reduces sensitivity to variations in segmented vessel caliber compared with geometry-only flow allocation.

Cerebral autoregulation modifies arteriolar resistance to stabilize blood flow under changes in systemic and local conditions. We incorporate active arteriolar dilation and constriction into a multiscale model that includes arterial and venous networks, pre- and postcapillary compartments, and a three-dimensional perfusion domain. This framework allows the coupled effects of aortic pressure, gravity, vessel-wall stiffness, and metabolic demand to be examined.

Under the investigated conditions, active autoregulation maintains cerebral blood flow within a relatively narrow range by adjusting arteriolar diameter. Arterial deformation and total vascular volume are affected by systemic pressure and gravity, whereas venous volume is primarily sensitive to posture. Increased vascular compliance amplifies posture-dependent volume changes and the corresponding autoregulatory response.

Arterial stenosis reduces downstream pressure and produces a mismatch between tissue supply and metabolic demand. We model arteriolar vasodilation as a reduction in downstream vascular resistance in response to local hemodynamic and metabolic stimuli. This mechanism allows the contribution of active regulation to perfusion recovery to be quantified under controlled stenosis conditions.

Under the investigated stenosis conditions, arteriolar vasodilation produces a larger improvement in tissue perfusion than sub-resolution collateral vessels alone. The contribution of collateral circulation remains dependent on vessel caliber, density, and stenosis severity, and is treated in Cerebrovascular Networks and Collateral Circulation.

We evaluate cerebral perfusion models through comparisons with physiological flow ranges, vascular morphology data, perfusion territories, and medical perfusion imaging. These comparisons assess whether the models reproduce plausible pressure, flow, and tissue-perfusion patterns. The resulting simulations provide mechanistic estimates rather than direct clinical diagnoses and remain dependent on assumptions regarding sub-resolution vasculature, tissue properties, and autoregulatory capacity.