Journal of Biomechanics

2003

DOI: 10.1016/s0021-9290(03)00088-5

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Hemodynamics simulation and identification of susceptible sites of atherosclerotic lesion formation in a model abdominal aorta

John R. Buchanan

¹

,

Clement Kleinstreuer

²

,

³

et al.

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Introduction2

Coincidence Of High-ldl Concentration With Plaque Location1

Discussion1

Wssag1

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2006

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Cited by 104 publications

(71 citation statements)

References 35 publications

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“…latory flow on leaky junction formation available that would allow for the inclusion of those effects in our LDL transport model. We, nevertheless, believe that our current approach is reasonable, since low-WSS regions and high-OSI regions are often found to coincide (7,23,30,61). For example, Huo et al (23) recently investigated the flow patterns in a porcine epicardial coronary artery tree and found that, especially near bifurcations, high-OSI regions coincide with low-WSS regions, and that these two parameters are related to each other through a power law.…”

Section: Coincidence Of High-ldl Concentration With Plaque Locationmentioning

confidence: 70%

Patient-specific three-dimensional simulation of LDL accumulation in a human left coronary artery in its healthy and atherosclerotic states

et al. 2009

American Journal of Physiology-Heart and Circulatory Physiology

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Olgac U, Poulikakos D, Saur SC, Alkadhi H, Kurtcuoglu V. Patient-specific three-dimensional simulation of LDL accumulation in a human left coronary artery in its healthy and atherosclerotic states. Am J Physiol Heart Circ Physiol 296: H1969 -H1982, 2009. First published March 27, 2009 doi:10.1152/ajpheart.01182.2008.-We calculate low-density lipoprotein (LDL) transport from blood into arterial walls in a three-dimensional, patient-specific model of a human left coronary artery. The in vivo anatomy data are obtained from computed tomography images of a patient with coronary artery disease. Models of the artery anatomy in its healthy and diseased states are derived after segmentation of the vessel lumen, with and without the detected plaque, respectively. Spatial shear stress distribution at the endothelium is determined through the reconstruction of the arterial blood flow field using computational fluid dynamics. The arterial endothelium is represented by a shear stress-dependent, threepore model, taking into account blood plasma and LDL passage through normal junctions, leaky junctions, and the vesicular pathway. Intraluminal pressures of 70 and 120 mmHg are employed as the normal and hypertensive operating pressures, respectively. By applying our model to both the healthy and diseased states, we show that the location of the plaque in the diseased state corresponds to one of the two sites with predicted high-LDL concentration in the healthy state. We further show that, in the diseased state, the site with high-LDL concentration has shifted distal to the plaque, which is in agreement with the clinical observation that plaques generally grow in the downstream direction. We also demonstrate that hypertension leads to increased number of regions with high-LDL concentration, elucidating one of the ways in which hypertension may promote atherosclerosis.low-density lipoprotein transport; lipid accumulation; patient-specific simulations; atherosclerosis; leaky junction ATHEROSCLEROSIS, A PROGRESSIVE disease characterized by the accumulation of lipids in the arterial walls, is the primary cause of heart disease and stroke (37). Locally elevated concentrations of low-density lipoprotein (LDL) are considered to be the initiator of atherosclerotic plaque formation (37,46,48). Therefore, transport of LDL into arterial walls has been the subject of various experimental (8,11,35,39,47,48) and computational investigations (2,29,43,50,52,53,55,56,59).Computational LDL transport models, as recently reviewed by Khakpour and Vafai (28), are categorized into three types: wall-free models, homogenous wall models, and multilayer wall models. In the simple wall-free models, fluid dynamics and LDL transport are calculated solely in the artery lumen. A constant filtration velocity and LDL flux, defined by an overall mass transfer coefficient, are generally applied on the lumenside artery surface. The main drawback of wall-free models is that they do not provide any information on the transmural flow and solute dynamics in the arterial...

“…latory flow on leaky junction formation available that would allow for the inclusion of those effects in our LDL transport model. We, nevertheless, believe that our current approach is reasonable, since low-WSS regions and high-OSI regions are often found to coincide (7,23,30,61). For example, Huo et al (23) recently investigated the flow patterns in a porcine epicardial coronary artery tree and found that, especially near bifurcations, high-OSI regions coincide with low-WSS regions, and that these two parameters are related to each other through a power law.…”

Section: Coincidence Of High-ldl Concentration With Plaque Locationmentioning

confidence: 70%

Patient-specific three-dimensional simulation of LDL accumulation in a human left coronary artery in its healthy and atherosclerotic states

et al. 2009

American Journal of Physiology-Heart and Circulatory Physiology

View full text Add to dashboard Cite

Olgac U, Poulikakos D, Saur SC, Alkadhi H, Kurtcuoglu V. Patient-specific three-dimensional simulation of LDL accumulation in a human left coronary artery in its healthy and atherosclerotic states. Am J Physiol Heart Circ Physiol 296: H1969 -H1982, 2009. First published March 27, 2009 doi:10.1152/ajpheart.01182.2008.-We calculate low-density lipoprotein (LDL) transport from blood into arterial walls in a three-dimensional, patient-specific model of a human left coronary artery. The in vivo anatomy data are obtained from computed tomography images of a patient with coronary artery disease. Models of the artery anatomy in its healthy and diseased states are derived after segmentation of the vessel lumen, with and without the detected plaque, respectively. Spatial shear stress distribution at the endothelium is determined through the reconstruction of the arterial blood flow field using computational fluid dynamics. The arterial endothelium is represented by a shear stress-dependent, threepore model, taking into account blood plasma and LDL passage through normal junctions, leaky junctions, and the vesicular pathway. Intraluminal pressures of 70 and 120 mmHg are employed as the normal and hypertensive operating pressures, respectively. By applying our model to both the healthy and diseased states, we show that the location of the plaque in the diseased state corresponds to one of the two sites with predicted high-LDL concentration in the healthy state. We further show that, in the diseased state, the site with high-LDL concentration has shifted distal to the plaque, which is in agreement with the clinical observation that plaques generally grow in the downstream direction. We also demonstrate that hypertension leads to increased number of regions with high-LDL concentration, elucidating one of the ways in which hypertension may promote atherosclerosis.low-density lipoprotein transport; lipid accumulation; patient-specific simulations; atherosclerosis; leaky junction ATHEROSCLEROSIS, A PROGRESSIVE disease characterized by the accumulation of lipids in the arterial walls, is the primary cause of heart disease and stroke (37). Locally elevated concentrations of low-density lipoprotein (LDL) are considered to be the initiator of atherosclerotic plaque formation (37,46,48). Therefore, transport of LDL into arterial walls has been the subject of various experimental (8,11,35,39,47,48) and computational investigations (2,29,43,50,52,53,55,56,59).Computational LDL transport models, as recently reviewed by Khakpour and Vafai (28), are categorized into three types: wall-free models, homogenous wall models, and multilayer wall models. In the simple wall-free models, fluid dynamics and LDL transport are calculated solely in the artery lumen. A constant filtration velocity and LDL flux, defined by an overall mass transfer coefficient, are generally applied on the lumenside artery surface. The main drawback of wall-free models is that they do not provide any information on the transmural flow and solute dynamics in the arterial...

“…This index represents the degree of variation of the WSS vector from its average direction during the cardiac cycle. It is considered to be an important indicator of atherosclerosis (Giddens et al 1993;Buchanan et al 2003;Blanco-Colio et al 2006) and is elevated in regions where cerebral aneurysms commonly develop, i.e. apices of arterial bifurcations (Meng et al 2007).…”

Section: Discussionmentioning

confidence: 99%

Modelling evolution and the evolving mechanical environment of saccular cerebral aneurysms

¹

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²

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³

et al. 2010

Biomech Model Mechanobiol

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A fluid-solid-growth (FSG) model of saccular cerebral aneurysm evolution is developed. It utilises a realistic two-layered structural model of the internal carotid artery and explicitly accounts for the degradation of the elastinous constituents and growth and remodelling (G&R) of the collagen fabric. Aneurysm inception is prescribed: a localised degradation of elastin results in a perturbation in the arterial geometry; the collagen fabric adapts, and the artery achieves a new homeostatic configuration. The perturbation to the geometry creates an altered haemodynamic environment. Subsequent degradation of elastin is explicitly linked to low wall shear stress (WSS) in a confined region of the arterial domain. A sidewall saccular aneurysm develops, the collagen fabric adapts and the aneurysm stabilises in size. A quasi-static analysis is performed to determine the geometry at diastolic pressure. This enables the cyclic stretching of the tissue to be quantified, and we propose a novel index to quantify the degree of biaxial stretching of the tissue. Whilst growth is linked to low WSS from a steady (systolic) flow analysis, a pulsatile flow analysis is performed to compare steady and pulsatile flow parameters during evolution. This model illustrates the evolving mechanical environment for an idealised saccular cerebral aneurysm developing on a cylindrical parent artery and provides the guidance to more sophisticated FSG models of aneurysm evolution which link G&R to the local mechanical stimuli of vascular cells.

“…WSSAG closely correlates with the WSSAD, indicating the surface nodes are approximately equidistant. A region of high WSSAG occurs at the flow divider, where entry to the ICA and ECA begins [66], and elevated values of WSSAG extend away from the divider along the CCA. This is similar to the available studies [16].…”

Section: Wssagmentioning

confidence: 99%

Application of a locally conservative Galerkin (LCG) method for modelling blood flow through a patient‐specific carotid bifurcation

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²

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³

et al. 2010

Numerical Methods in Fluids

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SUMMARYIn the present work, blood flow through a patient-specific carotid bifurcation is thoroughly analysed. A locally conservative Galerkin spatial discretization is applied along with an artificial compressibility and characteristic-based split scheme to solve the 3D incompressible Navier-Stokes equations. Boundary layer meshes are introduced to accurately resolve high near-wall velocity gradients inside a patient-specific carotid bifurcation. A total of six haemodynamic wall parameters have been brought together to analyse the regions of possible atherogenesis within the domain. The results show that wall shear stress (WSS) is high (6-15 Pa) near the apex, along with a small region where WSS exceeded 20 Pa. This peak WSS region is close to the inner wall of the external carotid artery (ECA). It is also clear from the results that low WSS occurred in the common carotid artery (CCA). High oscillatory shear occurred in the CCA distal to a local narrowing of the internal carotid artery and along the outer wall, indicating a potential region of atherogenesis. The highest values of wall shear stress angle deviation and wall shear stress angle gradient occur at the apex. The wall shear stress temporal gradient reached 4000 Pa/s near the apex, closer to the ECA. Wall shear stress spatial gradient distribution corresponded with the WSS distribution, with a maximum occurring at the apex.

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