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

Abstract

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.

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data

“…First of all, all 15 patient datasets of this cohort had aortic valve disease, which leads to a modification of the flow jet through the aortic valve and potentially to an adaptation of the wall properties (especially in the ascending aorta and in the aortic arch). Secondly, previous research has shown that the surrounding tissue of the aorta has a considerable effect on the haemodynamics, leading to higher wave speed, and lower total compliance [33]. Since the various structures surrounding the aorta have different elastic properties, they provide spatially varying external tissue support along the centreline of the aorta.…”