A method for modeling surrounding tissue support and its global effects on arterial hemodynamics

We propose a methodology for separating the total stiffness of arteries, determined in vivo, into stiffness of the arterial wall and stiffness of the surrounding tissue. An effective perivascular pressure is considered which introduces a radial constraint. Next, based on vivo data, acquired at diastolic pressure, the cross-sectional area at zero pressure is estimated. Finally, the stiffness of the arterial wall and of the surrounding tissue are determined based on a model with two parallel springs. By employing a reduced-order multiscale model, the methodology is used for studying the global effects of surrounding tissue support on arterial hemodynamics. The main effects are: higher wave speed, earlier arriving backward travelling pressure and flow rate waves, lower total compliance, higher pressure pulse, and reduced arterial cross-sectional areas. In a Big Data perspective, by combining this methodology with arterial wall growth models and by comparing simulation results and patient evolution over different time ranges, such an approach is useful for predicting patient-specific disease evolution and outcome.