Influence of Rigid–Elastic Artery Wall of Carotid and Coronary Stenosis on Hemodynamics

Albadawi, Muhamed; Abuouf, Yasser; Mohamed, Samir A. Elsagheer; Sekiguchi, Hidetoshi; Ahmed, Mahmoud

doi:10.3390/bioengineering9110708

Cited by 15 publications

(8 citation statements)

References 48 publications

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“…Blood, which in larger vessels contains both liquid and solid parts, exhibits a Newtonian flow state and a constant viscosity that changes according to the vessel diameter. Blood plasma, which is an incompressible, Newtonian-homogeneous fluid that makes up over 50% of blood [ 33 ], exhibits Newtonian behavior [ 25 ]. In microcirculatory systems, however, such as small vessels and capillaries with diameters under 1 mm, blood can be treated as a non-Newtonian fluid [ 38 ] whose changing viscosity can be determined using a modified Quemada model [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

“…For example, this study assumed that the vessel walls in the patient-specific model were rigid, an assumption that led to minor differences in simulation results due to wall shear stress and the relative velocity of the wall, because blood in contact with the wall was not discussed, as in a previous study [ 30 ]. A rigid wall assumption has been included in the majority of computational flow studies [ 47 ], such as a 2022 investigation of the CFD simulation of the cardiovascular system by Albadawi et al [ 33 ], which concluded that assuming a rigid wall led to only slight differences when modeling large-diameter vessels. Since the iliac vein is relatively large in diameter, assuming a rigid wall seemed acceptable for the present model.…”

Section: Resultsmentioning

confidence: 99%

“…To calculate flow fields and hemodynamic parameters, blood flow in the iliac veins assumed incompressible Newtonian flow [ 25 ]. Values for blood fluid density and dynamic viscosity (1050 kg/m 3 and 0.003 kg/m/s, respectively) were used according to previous studies [ 32 , 33 , 34 , 35 ]. As in previous CFD analyses, the model assumed venous wall rigidity [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

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CFD Study of the Effect of the Angle Pattern on Iliac Vein Compression Syndrome

Chen

Fan

et al. 2023

Bioengineering

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Iliac vein compression syndrome (IVCS, or May–Thurner syndrome) occurs due to the compression of the left common iliac vein between the lumbar spine and right common iliac artery. Because most patients with compression are asymptomatic, the syndrome is difficult to diagnose based on the degree of anatomical compression. In this study, we investigated how the tilt angle of the left common iliac vein affects the flow patterns in the compressed blood vessel using three-dimensional computational fluid dynamic (CFD) simulations to determine the flow fields generated after compression sites. A patient-specific iliac venous CFD model was created to verify the boundary conditions and hemodynamic parameter set in this study. Thirty-one patient-specific CFD models with various iliac venous angles were developed using computed tomography (CT) angiograms. The angles between the right or left common iliac vein and inferior vena cava at the confluence level of the common iliac vein were defined as α1 and α2. Flow fields and vortex locations after compression were calculated and compared according to the tilt angle of the veins. Our results showed that α2 affected the incidence of flow field disturbance. At α2 angles greater than 60 degrees, the incidence rate of blood flow disturbance was 90%. In addition, when α2 and α1 + α2 angles were used as indicators, significant differences in tilt angle were found between veins with laminar, transitional, and turbulent flow (p < 0.05). Using this mathematical simulation, we concluded that the tilt angle of the left common iliac vein can be used as an auxiliary indicator to determine IVCS and its severity, and as a reference for clinical decision making.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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CFD Study of the Effect of the Angle Pattern on Iliac Vein Compression Syndrome

Chen

Fan

et al. 2023

Bioengineering

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“…Displacement and deformation of the computational domain were not measured directly from the CT images and were neglected to simplify the complexity and assess the accuracy of the proposed Gaussian process approached by comparing the reconstructed velocity to the CFD results. The displacement and deformation of the coronary artery will introduce additional sources of error [ 101 ] that may require further algorithm developments. In this study, we have performed a computational experiment on PEPT accuracy, flow recovery and uncertainty.…”

Section: Discussionmentioning

confidence: 99%

Using Gaussian process for velocity reconstruction after coronary stenosis applicable in positron emission particle tracking: An in-silico study

Keramati,

de Vecchi,

Rajani

et al. 2023

PLoS ONE

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Accurate velocity reconstruction is essential for assessing coronary artery disease. We propose a Gaussian process method to reconstruct the velocity profile using the sparse data of the positron emission particle tracking (PEPT) in a biological environment, which allows the measurement of tracer particle velocity to infer fluid velocity fields. We investigated the influence of tracer particle quantity and detection time interval on flow reconstruction accuracy. Three models were used to represent different levels of stenosis and anatomical complexity: a narrowed straight tube, an idealized coronary bifurcation with stenosis, and patient-specific coronary arteries with a stenotic left circumflex artery. Computational fluid dynamics (CFD), particle tracking, and the Gaussian process of kriging were employed to simulate and reconstruct the pulsatile flow field. The study examined the error and uncertainty in velocity profile reconstruction after stenosis by comparing particle-derived flow velocity with the CFD solution. Using 600 particles (15 batches of 40 particles) released in the main coronary artery, the time-averaged error in velocity reconstruction ranged from 13.4% (no occlusion) to 161% (70% occlusion) in patient-specific anatomy. The error in maximum cross-sectional velocity at peak flow was consistently below 10% in all cases. PEPT and kriging tended to overestimate area-averaged velocity in higher occlusion cases but accurately predicted maximum cross-sectional velocity, particularly at peak flow. Kriging was shown to be useful to estimate the maximum velocity after the stenosis in the absence of negative near-wall velocity.

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“…Thus, the CFD-based simulations are of high computational cost due to the requirements of mighty computing resources, large-scale computing time, and highly skilled experts (Yamaguchi et al, 2016;Fu et al, 2020). Moreover, the simulation is generally performed in a patient-specific manner by using the image-based geometric model for each individual under specific boundary conditions, which needs to be conducted for all patients and is usually highly time-consuming (Fu et al, 2010;Conti et al, 2016;Bluestein, 2017;Polanczyk et al, 2018;Albadawi et al, 2021). Thus, it is a crucial issue to pay the expensive computational costs for real-time simulations of complex blood flows in association with the realistic clinical applications of CFD methods for surgical treatments such as CAS.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments

Wang

et al. 2023

Front. Physiol.

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Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error<12.5%) and flow visualization in predicting the two hemodynamic parameters. In addition, the DL-based predictions were in good agreement with CFD simulations in terms of mean velocity in the stenotic region for both the preoperative and postoperative datasets. This study points to the capability and significance of the DL-based fast and accurate hemodynamic prediction of preoperative and postoperative CAS. For accomplishing real-time monitoring of surgical treatments, further improvements in the prediction accuracy and flexibility may be conducted by utilizing larger datasets with specific real surgical events such as stent intervention, adopting personalized boundary conditions, and optimizing the DL network.

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Influence of Rigid–Elastic Artery Wall of Carotid and Coronary Stenosis on Hemodynamics

Cited by 15 publications

References 48 publications

CFD Study of the Effect of the Angle Pattern on Iliac Vein Compression Syndrome

CFD Study of the Effect of the Angle Pattern on Iliac Vein Compression Syndrome

Using Gaussian process for velocity reconstruction after coronary stenosis applicable in positron emission particle tracking: An in-silico study

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments

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