Abstract
Simulation of haemodynamics has become increasingly popular within the research community. Irrespective of the modelling approach (zero-dimensional (0D), one-dimensional (1D) or three-dimensional (3D)), in vivo measurements are required to personalize the arterial geometry, material properties and boundary conditions of the computational model. Limitations in in vivo data acquisition often result in insufficient information to determine all model parameters and, hence, arbitrary modelling assumptions. Our goal was to minimize and understand the impact of modelling assumptions on the simulated blood pressure, flow and luminal area waveforms by studying a small region of the systemic vasculature—the upper aorta—and acquiring a rich array of non-invasive magnetic resonance imaging and tonometry data from a young healthy volunteer. We first investigated the effect of different modelling assumptions for boundary conditions and material parameters in a 1D/0D simulation framework. Strategies were implemented to mitigate the impact of inconsistencies in the in vivo data. Average relative errors smaller than 7% were achieved between simulated and in vivo waveforms. Similar results were obtained in a 3D/0D simulation framework using the same inflow and outflow boundary conditions and consistent geometrical and mechanical properties. We demonstrated that accurate subject-specific 1D/0D and 3D/0D models of aortic haemodynamics can be obtained using non-invasive clinical data while minimizing the number of arbitrary modelling decisions.
Highlights
Computational modelling of cardiovascular dynamics has received notable attention over the last two decades
We have shown that accurate, subject-specific, 1D/0D and 3D/0D models of pulse wave haemodynamics in the upper aorta of a young healthy volunteer can be obtained using non-invasive clinical data
By simulating blood flow in a confined region of the systemic vasculature and acquiring a substantial amount of in vivo measurements, we have minimized the number of arbitrary modelling assumptions and determined most of the model parameters from the in vivo data
Summary
Computational modelling of cardiovascular dynamics has received notable attention over the last two decades. Lumped parameter methods provide a computationally inexpensive, mathematically accessible and intuitive framework to study whole-system dynamics. They are not suitable for studying pulse propagation phenomena or complex flows. Nonlinear 1D methods can accurately describe pulse wave propagation phenomena in extensive vascular networks while keeping the computational cost down. These methods are not appropriate to describe complex 3D flow features, like those observed in stenosis and aneurysms.
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