Hemodynamic characteristics in a cerebral aneurysm model using non-Newtonian blood analogues

Yi, Hang; Yang, Zifeng; Johnson, Mark; Bramlage, Luke; Ludwig, Bryan

doi:10.1063/5.0118097

Cited by 16 publications

(17 citation statements)

References 85 publications

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“…Due to the sensitivity to initial conditions and global hydrodynamic instability, it has been found that the physiologic pulsatile blood flow is turbulent even under a relatively small mean Reynolds number in both studies in vitro and in silico ( Valen-Sendstad et al, 2011 ; Yagi et al, 2013 ; Jain et al, 2016 ; Saqr et al, 2020 ; Tupin et al, 2020 ; Yi et al, 2022b ). Thus, the continuity and momentum equations can be written in tensor form, i.e.,

where

represents the blood flow velocity, p is the pressure,

is the gravity,

is the blood dynamic viscosity which was set as

in accordance with the PIV tests, and

is the turbulent viscosity.…”

Section: Numerical Methodologymentioning

confidence: 99%

Developing an in vitro validated 3D in silico internal carotid artery sidewall aneurysm model

Yang

Johnson

et al. 2022

Front. Physiol.

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Introduction: Direct quantification of hemodynamic factors applied to a cerebral aneurysm (CA) remains inaccessible due to the lack of technologies to measure the flow field within an aneurysm precisely. This study aimed to develop an in vitro validated 3D in silico patient-specific internal carotid artery sidewall aneurysm (ICASA) model which can be used to investigate hemodynamic factors on the CA pathophysiology.Methods: The validated ICASA model was developed by quantifying and comparing the flow field using particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Specifically, the flow field characteristics, i.e., blood flowrates, normalized velocity profiles, flow streamlines, and vortex locations, have been compared at representative time instants in a cardiac pulsatile period in two designated regions of the ICASA model, respectively. One region is in the internal carotid artery (ICA) inlet close to the aneurysm sac, the other is across the middle of the aneurysmal sac.Results and Discussion: The results indicated that the developed computational fluid dynamics model presents good agreements with the results from the parallel particle image velocimetry and flowrate measurements, with relative differences smaller than 0.33% in volumetric flow rate in the ICA and relative errors smaller than 9.52% in averaged velocities in the complex aneurysmal sac. However, small differences between CFD and PIV in the near wall regions were observed due to the factors of slight differences in the 3D printed model, light reflection and refraction near arterial walls, and flow waveform uncertainties. The validated model not only can be further employed to investigate hemodynamic factors on the cerebral aneurysm pathophysiology statistically, but also provides a typical model and guidance for other professionals to evaluate the hemodynamic effects on cerebral aneurysms.

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where

represents the blood flow velocity, p is the pressure,

is the gravity,

is the blood dynamic viscosity which was set as

in accordance with the PIV tests, and

is the turbulent viscosity.…”

Section: Numerical Methodologymentioning

confidence: 99%

Developing an in vitro validated 3D in silico internal carotid artery sidewall aneurysm model

Yang

Johnson

et al. 2022

Front. Physiol.

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“…To explore hemodynamic patterns in MT models, our experimental validated non-Newtonian CFD CA model [8] was employed to simulate the blood flow regime. The unsteady and periodic pulsatile incompressible blood flow can be modeled by the continuity and momentum equations, which are, i.e., 𝛻 • 𝑣 ⃗ = 0…”

Section: Mathematical Equationsmentioning

confidence: 99%

“…where 𝑣 ⃗ is the blood velocity vector, 𝑝 is the pressure, 𝑔 ⃗ is gravity vector, and 𝜌 denotes blood density. In this study, 𝜌 (i.e., 1050 kg/m 3 ) is adopted for the CFD simulations, 𝜇 is blood viscosity determined by a non-Newtonian viscosity model at temperature 310.15 K (see Figure 7) which was developed in our previous publication [8]. Specifically, the model is expressed by, i.e.,…”

Section: Mathematical Equationsmentioning

confidence: 99%

“…To quantify the differences in morphology and hemodynamics of cerebral arterial and aneurysmal models in a pair of MTs (i.e., twin A and twin B), the configurations and dimensions of the reconstructed models were compared first to identify differences related to embryological and environmental factors. Then, to quantify hemodynamic characteristics in the MTs, an in-vitro validated computational fluid dynamics (CFD) CA model [8] was employed to model the realistic blood flow patterns in the MTs. The findings provide a pathway to understand impacts of genetic and environmental factors on the morphology, which can further affect hemodynamics affiliated pathogenesis of neurovascular diseases.…”

Section: Introductionmentioning

confidence: 99%

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Morphology and Hemodynamics of Cerebral Arteries and Aneurysms in a Pair of Monozygotic Twins

Yi¹,

Yang²,

Bramlage³

et al. 2023

Preprint

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The contribution of genetic and environmental factors to the pathophysiology of cerebral aneurysms in monozygotic twins is under-reported and presents ambiguous arguments. The morphology and hemodynamics of neurovascular arteries in a pair of monozygotic twins (MTs) were investigated to reveal the underlying mechanisms. Four arterial models were reconstructed for the twin A-right brain and left brain, twin B-left brain, and B-left brain without anterior cerebral arteries based on preclinical scanned information. Subsequently, the dimensions, configurations and outlined curves of the three-perspective geometries were compared between the MTs. Adopting an in-vitro validated numerical cerebral aneurysm model, hemodynamic patterns were investigated and compared in the MT models, respectively. Morphological comparisons of the MTs show the size and shape of cerebral arteries exist significant differences, despite of the expected genetic similarities. These differences can be attributed to variations during embryological development and external environmental influences. Qualitatively and generally, numerical results indicate the MTs have some hemodynamic similarities (e.g., time-averaged pressure (TAP) distributions (~13400 Pa), and oscillatory shear index (0~0.49), but present significant differences in specific local arteries due to morphological variances. Specifically, the difference in the volumetric blood flow rate in corresponding arteries between the MTs is from 16% (smallest) in anterior choroidal artery (AChA) to 221% (largest) in the ophthalmic artery (OphA), varying with specific compared arteries. Also, the registered hemodynamic indicators, such as the maximum time-averaged wall shear stress (TWSS) (53.6 Pa vs. 37.8 Pa), and different local OSI distributions were observed between the MTs. The findings revealed that morphological variations in MTs could be generated by embryological and environmental factors, thus assuming they share the identical morphology in cardiovascular and neurovascular systems may lead to significant misevaluations in hemodynamics quantifications and further lesions.

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“…According to the statistical data from the World Health Organization, cardiovascular associated diseases have become the topmost cause of death [ 1 , 2 ], and it has been identified that hemodynamic factors play a significant role in the development and progression of such diseases [ 3 , 4 , 5 , 6 ]. For example, the development of atherosclerosis is based on the observation that vascular inflammation and plaques are distributed near side branches or arterials stenosis, where blood flow is non-uniform, and at the lesser curvature of bends where the blood flow rate is relatively low [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography

Padhee

Johnson

et al. 2022

Bioengineering

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Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground truth velocity field and projective images of dye flow patterns. The rough velocity field was estimated using the optical flow method (OFM) based on 53 projective images. ML training with least absolute shrinkage, selection operator and convolutional neural network was conducted with CFD velocity data as the ground truth and OFM velocity estimation as the input. The performance of each model was evaluated based on mean absolute error and mean squared error, where all models achieved or surpassed the criteria of 3 × 10−3 and 5 × 10−7 m/s, respectively, with a standard deviation less than 1 × 10−6 m/s. Finally, the interpretable regression and ML models were validated with over 613 image sets. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% on average for the v-velocity estimation in comparison with CFD. The ML framework provided an alternative pathway to support clinical diagnosis by predicting hemodynamic information with high efficiency and accuracy.

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Hemodynamic characteristics in a cerebral aneurysm model using non-Newtonian blood analogues

Cited by 16 publications

References 85 publications

Developing an in vitro validated 3D in silico internal carotid artery sidewall aneurysm model

Developing an in vitro validated 3D in silico internal carotid artery sidewall aneurysm model

Morphology and Hemodynamics of Cerebral Arteries and Aneurysms in a Pair of Monozygotic Twins

Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography

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