Towards an integrated platform combining in-vivo and in-vitro data with computational modelling of human vascular hemodynamics

The PhD research activity aims to carry out numerical and experimental analysis on hemodynamic flows. The ultimate objective is to develop tools to provide reliable and detailed information on the cardiovascular flows in arterial vessels of particular interest, i.e thoracic aorta or carotid vessels, on a patient-specific level, by combining in-vivo data with in-vitro experiments and in-silico simulations. The first activity focuses on modelling atherosclerotic plaque growth in patient-specific carotid arteries. The objective of this study is to set-up a numerical platform to predict the onset of atherosclerotic plaques in patient-specific carotid arteries by coupling Computational Fluid Dynamics (CFD) simulations with a plaque growth model and a morphing procedure. We first assess the reliability of a model proposed in the literature, denoted here as the Low-Density Lipoprotein (LDL) model, which correlates plaque growth with the wall shear stress magnitude and the blood LDL concentration. By comparing numerical predictions with in-vivo data, we found that the LDL model correctly predicts the onset region of the disease and gives growth rates during the early stages of the pathology in reasonable agreement with literature data. This is not the case, conversely, for growth models only based on wall shear stresses. Consequently, the LDL model is used to evaluate a risk factor, based on both the probability of plaque onset and on the predicted plaque growth rate, which could be used as a single indicator of potential risk. The advantage is that it gives an indication of the region where the plaque is likely to form and grow based on a single steady simulation of the healthy geometry, and therefore at moderate computational cost. Finally, we analyze whether and how different hemodynamics features, such as, e.g., dynamics of vortical structures or instantaneous wall-shear stresses, are related with the plaque formation and growth predicted by the model and with the risk indicator. Building on this efficient framework, the next phase investigates how carotid bifurcation geometry affects plaque formation. Parametric models, derived from a real patient geometry, are used to perform a sensitivity analysis using stochastic collocation and sparse grids. Results show that even small geometric changes, especially in the internal carotid artery’s curvature and inflection point, strongly influence plaque growth patterns, particularly at the edges of the predicted onset region. Finally, unsteady flow analysis on selected parametric geometries reaffirms that local geometric variations significantly impact flow dynamics and thus play a key role in plaque development. Subtle shape differences can alter vortical structures and shear stress patterns and variability, highlighting the sensitivity of plaque growth to arterial geometry. The second activity involves a Particle Image Velocimetry (PIV) analysis conducted on a patient-specific thoracic aorta model to investigate hemodynamic flow patterns. Thoracic aorta represents a fundamental arterial vessel supplying oxygenated blood to most of the organs and body, and it is crucial to have a complete understanding of the hemodynamic flow developing in this artery. However, the flow in the aorta is difficult to measure, and in-vivo four-dimensional-flow Magnetic Resonance Imaging (4D-flow MRI) provides the velocity field with a low spatial and temporal resolution. PIV measurements, acquired in a fully-controlled and characterized experimental set-up are useful to obtain a complete understanding of the flow features, and they could also be used to validate CFD hemodynamic simulations that are widely used to investigate patient-specific cardiovascular flows. In this work, we present and discuss PIV measurements in a patient-specific thoracic aorta integrated into a hybrid mock circulatory loop. The aortic phantom, fabricated via lost-core casting, embedded in a modular and optically accessible structure, enables flow visualization across different anatomical regions. The hybrid mock circulatory loop combines a programmable piston pump and real-time flow sensing to impose physiological pulsatile inflow profiles, while also enabling precise control over outflow pressure conditions. The PIV acquisitions are performed at multiple depths along each anatomical plane, enabling ultimately to reconstruct the velocity orthogonal to the cross-section planes over the cardiac cycle. The reconstructed flow rates show satisfactory agreement with the reference flow sensor data, and repeated measurements exhibited low variability.