The complex hemodynamics observed in the human aorta make this district a site of election for an in depth investigation of the relationship between fluid structures, transport and patho-physiology. In fact, it is well known that hemodynamics play an important role in the mass transport of blood specimen, and in turn, in their transfer to the vascular wall and ultimately in the localization of vascular disease in areas of complex arterial flow. In particular, the accumulation of lipoproteins in the arterial intima is a hallmark of atherosclerosis. Low-density lipoproteins (LDL) are the most abundant atherogenic lipoproteins in plasma and high plasma levels of LDL are causally related to the development of atherosclerosis. Advanced computational fluid dynamics coupled with medical imaging allows to combine the anatomical and hemodynamic inputs to realistic, fully personalized flow simulations to study local hemodynamics in arteries. Such an approach represents an effective way when addressing the still open questions about the role of blood-side LDL transfer to the arterial wall in atherogenesis. In particular, personalized computational models have been proposed to study LDL blood-to-wall transfer in the aorta, a district of election for the study of the relationships between the intricate local hemodynamics, LDL transport and disease. However, computational hemodynamics requires some assumptions that could affect the solutions of the equations governing blood flow and the aortic LDL transport and wall transfer. In previous studies of my research group, has been demonstrated that different strategies in applying boundary conditions (BCs) derived from phase-contrast MRI (PC-MRI) measurements could lead to different results in terms of distribution of near-wall and intravascular flow quantities. Additionally, a paucity of data characterizes the literature concerning the BCs and initial conditions (ICs) adopted to model LDL transport. In this contest, my thesis project have been focalized in analysing the impact that different possible strategies of applying (1) PC-MRI measured data as inflow BCs, and (2) LDL concentration profiles as ICs, have on LDL blood-to-wall transfer modelling in the human aorta. Technically, two different inflow BCs are generated, by imposing at the ascending aorta inflow PC-MRI measured 3D velocity profiles or idealized (flat) velocity profiles. In this way, the sensitivity of blood-to-wall LDL transfer to the inflow BC is explored, as the inflow BC influences LDL advective transport. The impact of applied BC-IC strategies on LDL blood-to-wall transfer has been evaluated in terms of computational costs and LDL polarization profiles at the luminal surface. Moreover, by virtue of the reported high LDL concentration in correspondence of disturbed shear regions, here a co-localization analysis with areas exposed to atheroprone wall shear stress (WSS) phenotype has been conducted as a “physics consistency check”. The results of my research would contribute to clarify which is (1) the level of detail obtained from measured flow data to be used as inflow BC, and (2) the plausibility of hypotheses on LDL concentration to be used as IC strategy, to satisfactorily simulate mass transport/transfer in personalized complex hemodynamic models of human aorta.
|Titolo:||Impact of Patient-Specific Inflow Boundary Conditions and Concentration Initialization on Hemodynamics and Low-Density Lipoproteins Transport in the Thoracic Aorta|
|Data di pubblicazione:||3-mag-2018|
|Appare nelle tipologie:||8.1 Doctoral thesis Polito|
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|Impact of Patient-Specific Inflow Boundary Conditions and Concentration Initialization on Hemodynamics and Low-Density Lipoproteins Transport in the Thoracic Aorta - Giuseppe De Nisco.pdf||Tesi di Dottorato||5. Doctoral Thesis||PUBBLICO - Tutti i diritti riservati||Visibile a tuttiVisualizza/Apri|