Experimental and numerical study on the hemodynamics of stenosed carotid bifurcation

SCIENTIFIC PAPER

Experimental and numerical study on the hemodynamicsof stenosed carotid bifurcation

Sherman C. P. Cheung • Kelvin K. L. Wong •

Guan Heng Yeoh • William Yang • Jiyuan Tu •

Richard Beare • Thanh Phan

Received: 5 August 2010 / Accepted: 14 December 2010

� Australasian College of Physical Scientists and Engineers in Medicine 2010

Abstract Numerical simulation is performed to demon-

strate that hemodynamic factors are significant determinants

for the development of a vascular pathology. Experimental

measurements by particle image velocimetry are carried out

to validate the credibility of the computational approach. We

present a study for determining complex flow structures

using the case of an anatomically realistic carotid bifurcation

model that is reconstructed from medical imaging. A

transparent silicone replica of the artery is developed for in-

vitro flow measurement. The dynamic behaviours of blood

through the vascular structure based on the numerical and

experimental approaches show good agreement.

Keywords Particle image velocimetry � Computational

fluid dynamics � Carotid bifurcation � Stenosis

Introduction

During the last two decades, advancement of non-invasive

medical imaging and the use of computational fluid

dynamics (CFD) in the study of stenosed carotid bifurca-

tions have gained significance. Although significant

improvements has been made to the application of CFD

models to resolve patient-specific geometries, there have

been limited assessments on the validity of numerical

results to be produced using CFD. In order to improve CFD

predictions, and to consider CFD as a clinical diagnostic or

treatment planning tool, there is an ever-present need to

ensure its accuracy and reliability through a systematic

framework of comparing the numerical results with clinical

and experimental data. Comparison with clinical data,

which can be acquired through non-invasive measurement

techniques such as phase contrast magnetic resonance

imaging [27, 29] and Doppler Ultrasound [6, 9, 18] is

crucial to the understanding of flow fields under in vivo

biological environment. However, such measurements

suffer from technical deficiencies such as long measuring

times [29, 34] and insufficient resolutions to derive velocity

gradient and wall shear stress [16, 18] and thus making

them impractical for quantitative CFD assessments.

Alternatively, experimental measurements, where higher

resolution flow fields is assessable, can be performed to

complement the short-comings of in vivo measurements

and are therefore vital for thorough CFD cross-validations.

Validations of CFD results with particle image veloci-

metry (PIV) measurements have been reported [14, 23].

Measurements were performed in idealized aneurysm

S. C. P. Cheung � K. K. L. Wong � J. Tu (&)

School of Aerospace, Mechanical & Manufacturing Engineering,

and Health Innovations Research Institute (HIRi),

RMIT University, PO Box 71, Bundoora, VIC 3083, Australia

e-mail: [email protected]

G. H. Yeoh

Australian Nuclear Science and Technology Organisation

(ANSTO), PMB 1, Menai, NSW 2234, Australia

G. H. Yeoh

School of Mechanical and Manufacturing Engineering,

University of New South Wales, Sydney, NSW 2052, Australia

W. Yang

Division of Minerals, Commonwealth Scientific and Industrial

Research Organization (CSIRO), Clayton, VIC 3169, Australia

R. Beare � T. Phan

Department of Medicine, Southern Clinical School,

Monash Medical Centre, Monash University,

Melbourne, VIC 3168, Australia

123

Australas Phys Eng Sci Med

DOI 10.1007/s13246-010-0050-4

phantoms to quantify the influence of geometrical changes

on the aneurismal flows. Experimental measurement on

patient specific models was firstly obtained through the

study performed using phase contrast MRI [1]. On the basis

of similar phantom creation, PIV measurements can be

conducted using two anatomically realistic cerebral aneu-

rysm models [11]. The experimental results were used to

validate pulsatile flows predicted by CFD models. With

regards to three-dimensional flow structure, the particle

tracking velocimetry (PTV) technique is adopted to mea-

sure the steady flow field in an abdominal aortic aneurysm

and compared their CFD velocity predictions with mea-

surements [5]. Through all these experimental and

numerical studies, investigations have been primarily

focused on aneurismal flows where the flow characteristics

could be substantial different from those in stenosed car-

otid bifurcations.

The narrowed vessel lumen in carotid bifurcation can be

shown to lead to the blood flow being in the transitional

flow regime, which is extremely difficult to model in the

context of CFD [28]. This observation was further con-

firmed by a study whereby flow transition from laminar to

weak turbulence was clearly demonstrated in a direct

numerical simulation (DNS) approach [20]. To gain an

in-depth understanding of the onset of transitional flow,

several experiments have been conducted to measure the

stenotic flows in carotid bifurcations [2, 33]. Nevertheless,

comparison between experimental and CFD results is still

limited. As one of the continuing efforts to investigate

pulsatile flows in stenosed carotid arteries, the objective of

the present paper is to obtain detailed PIV measurements in

a patient specific transparent carotid bifurcation model and

to validate the numerical results in order to assess the

accuracy of the CFD model, and also to explore plausible

difficulties of the CFD model in capturing the onset of

transitional flow.

Methodology

The overall methodology of this study is depicted by

Fig. 1. Experimental and numerical studies of hemody-

namic characteristics have been performed in parallel

based on an anatomically realistic carotid bifurcation

model reconstructed from magnetic resonance images.

Rigorous validation was performed by comparing the

hemodynamic parameters between the numerical predic-

tions and experimental measurements.

Anatomical reconstruction of carotid bifurcation

High resolution magnetic resonance imaging of a stenosed

carotid bifurcation was acquired. In this study, the scan is

performed on a 42 year-old male using a 1.5-T General

Electric scanner. A total of 112 contiguous slices were

generated from the high-resolution T-1 weighted spoiled

gradient echo with parameters as follows: TR, 35 ms; TE,

7 ms; flip angle, 35�; field of view, 24 cm; voxel size

0:63 mm� 0:73 mm� 0:63 mm.

An automated detection algorithm was then applied to

the images to generate a conservative segmentation of

stenosed carotid bifurcation using a two-dimensional

watershed transform form markers applied to each slice

[25]. Based on the segmentations of different adjacent

planes, a high resolution three-dimensional computer artery

model was created and the data was stored in a stereoli-

thography (STL) file format. To enhance the resolution of

PIV measurement, the flow phantom was enlarged to 10

times of its human counterpart. Figure 2 shows the

reconstructed carotid geometry employed in this study.

Based on the geometrical data from the STL file, a negative

model was created by a Rapid Prototyping (RP) printer

(model ZCorp 3D) using a water-soluble plaster. Following

the method adopted by another study, five layers of water-

soluble glue were painted on the plaster model to smooth

and seal the pores on its surface [15].

Acquisition Medical Images

3D Computer model

Computational Model

Physical Model

Segmentation

Model Discretization

Rapid Prototyping

CFD Simulation

PIV Measurements

Experimental Hemodynamic

parameters

Predicted Hemodynamic

parameters

Validation

Fig. 1 Procedural flow chart for experimental and numerical studies.

The presentation of a systematic approach to perform experimental

and numerical data acquisition and comparison can allow us to

compare the predicted and experimentally derived flow. Data retrieval

and anatomical reconstruction based on MRI generates a geometrical

model. Then, the rapid prototyping of the model that is followed by

PIV measurement of flow within it, and numerical simulation using

the same anatomical model are performed. The predicted data from

simulation and the experimentally measured flows are examined in

the final stage


123

The painted prototype was then encased in a Plexiglas

box where clear silicone (using Dow Sylgard 184) was

casted around the plaster model to create an optically clear

positive model. After the silicone has been cured, the

plaster model was then dissolved out with cold water,

leaving a patient-specific replica of the stenosed carotid

bifurcation. Finally, three pieces of reinforced flexible

plastic tubes were attached to the inlet and outlet of the

phantom as connectors to the flow loop in the PIV

experiment.

Experimental setup for PIV measurement

In Fig. 3, we show a schematic diagram of the experiment

setup for PIV measurement. The experimental flow loop

comprises of a silicone phantom, an elevated fluid tank, a

flow meter and a suction pump. To eliminate refraction of

the laser sheet, the index of refraction of working fluid was

specifically chosen so as to match the refraction index of

the phantom wall. In this study, the working fluid consisted

of a mixture of glycerol (55% by mass) and distilled water

(45% by mass), which has a refraction index of 1.42 and

kinematic viscosity of m ¼ 6:2� 10�6 m2=s at a constant

temperature of 25�C. Note that the kinematic viscosity of

human blood has a value of 3:4� 10�6 m2=s. Consider that

the kinematic viscosity of mixture and the phantom are

scaled-up by 10 times, the flow rate of working fluid can be

established based on dynamic similarity. A flow Reynolds

number (Re) of 485 was determined at the flow phantom

inlet boundary, corresponding to the typical flow rate at the

common carotid artery (CCA) (i.e. 12.17 ml/s) of a healthy

adult at the peak of cardiac cycle [32]. The flow rate of

working fluid can be presented using

Qmixture ¼ 10� mmixture

mblood

� Qblood; ð1Þ

where Q is the volumetric flow rate and m is the kinematic

viscosity. A constant volumetric flow rate of mixture (i.e.

Qmixture ¼ 21:93 ml/s) is adopted throughout the experi-

ment. Rhodamine B fluorescent particles with a mean

diameter of 12:3 lm and a relative density of 1:1 kg/m3

were adopted as the seeding particles. The mixture was

circulated by a suction pump and entered the flow phantom

through a reinforced flexible tube with a diameter about

25 mm. The working fluid then exited the flow phantom

from the two outlets (internal and external carotid arties)

and entered the elevated flow tank to eliminate cavitations

that could occur within the flow system.

The ILA (Intelligent Laser Applications GmbH, Germany)

PIV system which consisted of a 1.3 Megapixel (1280�1024 pixels) 12-bit digital CCD camera was employed for

measurements. A New Wave 120 mJ double-cavity

ICA

ECA

ECAICA

ICA ECA

YZ

X

Fig. 2 Three dimensional

views of the stenosed carotid

bifurcation. Anatomical

geometry of the carotid

bifurcation is reconstructed

using MRI data, and output in

the STL format. Three views of

the patient specific vessel are

presented with the labels ECA,

ICA, and CCA representing the

external, internal and common

carotid arteries respectively. We

note the location of the stenosis

at the ICA that has a relatively

smaller cross-sectional area as

compared to the ECA


123

Nd:YAG laser head that is synchronized with the CCD

camera was installed on a horizontal traverser, which

allowed the measurement plane to be altered by a repeatable

and quantifiable amount. The laser beam was expanded to a

2 mm thick plane vertical light sheet that was directed

through the replicated model. Measurements were taken in

3 mm slices in sagittal planes from left to right. The field of

view of the CCD camera was 165 mm� 132 mm using

1280� 1024 pixels of a CCD array.

Numerical details of the CFD simulation

For comparison, numerical simulation was performed at the

in-vitro scale identical to the phantom flow used in PIV

measurement. Experimentally derived volumetric flow rates

were set at one inlet and two outlets. To minimize the end

effect from all openings, extended regions were also incor-

porated at both inlet and outlets of the computational model

(see Fig. 4). A mesh consisting of three-dimensional tetra-

hedral elements was generated via GAMBIT over the entire

flow domain. Grid sensitivity test was carried out at three

different grid mesh levels: coarse (129,182 cells), medium

(286,712 cells) and fine (1,517,434 cells). Comparing the

predicted maximum velocity at the stenosed region between

the medium and fine meshes, a discrepancy of 0.2% was

observed. It can thereby be concluded that the fine mesh can be

employed in obtaining grid independent solutions. Hereafter,

predicted results shown in the next section were all obtained

based upon the fine mesh. Isometric views of the computa-

tional model and the surface mesh are shown in Fig. 4.

CCD Camera

PIV Unit

Synchroniser

Computer

YAG Laser Controller

Dual YAG Laser Head

Measurement Plane

Light Sheet Optics

Articulated Arm

Bifurcation Model

Fig. 3 Schematic diagram of

the PIV apparatus adopted in the

experiment. The experimental

setup involves the use of PIV to

capture flow within the carotid

bifurcation silicon phantom. It

comprises of an elevated fluid

tank, a flow meter and a suction

pump to support the flow

condition. A laser system and

CCD camera allows the flow

measurements optically. The

PIV data is then post-processed

and can be used as experimental

information for validation of

CFD simulation results

Extended tubes at two outlets

Extended tube at inlet

ICA

ECA

CCA

ICA

ECA

CCA

Fig. 4 Computational carotid bifurcation models used for numerical

simulation. Isometric view of the reconstructed computer model for

experimental and numerical study (left) and the enlarged view of the

mesh distribution of the CFD model (right). The geometry comprises

of one inlet (at CCA) and two outlets (at ICA and ECA)


123

Estimates of patient-specific haemodynamics parame-

ters have been attained by [24, 31]. Some researchers

employed non-Newtonian fluid models to aptly character-

ise the rheological effect in blood flows [7, 13, 17, 21, 26].

However, there are numerical investigations have consid-

ered Newtonian fluid flows in rigid stenosed arteries

[10, 12, 30, 37]. The fluid flow is considered to be steady,

isothermal, incompressible and Newtonian. Consistent with

the experiment, wall boundaries of the computational

model are assumed to be rigid and impermeable. A generic

finite volume CFD code ANSYS-FLUENT was utilized to

solve the Navier–Stokes equations. Numerical solutions

were obtained through the iterative algebraic multi-grid

solver with the advection terms approximated via the sec-

ond-order upwind differencing scheme. In the present

study, reliable convergence was achieved within 2500

iterations when the RMS (root mean square) pressure

residual dropped below 1.0 9 10-7. A fixed physical time

scale of 0.002 s was adopted for steady-state simulations.

Results

Visualization of transitional flow pattern in stenosed

carotid artery

The flow characteristic inside the stenosed carotid bifurca-

tion is illustrated in Fig. 5. Contours of axial velocities have

been plotted on seven equally spaced slices normal to the

flow direction. Flow streamlines representing the trajectory

of finite fluid particles are tracked. As depicted in slice 1, a

Womersley flow profile with uniform streamlines is estab-

lished at the upstream of the sinus region. After passing

through this region, the flow enters both the internal and

external carotid arteries (i.e. ICA and ECA). Velocity dis-

tributions are observed to be highly skewed toward the outer

vessel walls. Similar findings have also been reported,

whereby such skewed flow structure is primarily caused by

the misalignment of the mean axis of arteries [33]. Further

downstream, strong flow separations and secondary flows

are clearly exemplified by the streamlines. Owing to the

rapid enlargement of the cross-sectional area, flow separa-

tion firstly occurred at the sinus region (see streamlines

between slices 1–3). Similar flow separation is also found at

downstream of ICA as the flow continues through the ste-

nosis (see streamlines after slice 7a). Streamlines between

slices 4b–7b shows a strong swirling secondary flow in the

ECA. The inception of such flow is due to the action of

centrifugal forces caused by the curvature of lumen, which

redistributes the flow velocity and further intensifies the

skewness of the flow structure [33].

The complexity of asymmetric flow pattern within the

stenosed carotid artery clearly depicted the fluid flow

transiting to a state of weak turbulence downstream while

remaining laminar upstream [20]. A closer examination can

be envisaged from the iso-surface plot of the vorticity field

by computational and experimental results as shown in

Fig. 6. Upstream, the flow remained laminar and uniform

Fig. 5 Flow visualisation in a

stenosed carotid bifurcation

based on numerical simulation.

The contour plots of axial

velocities and streamlines traces

in a stenosed carotid bifurcation

are presented after extraction of

the flow information within the

anatomical geometry. This flow

visualisation allows us to

understand the flow condition

within a diseased artery

effectively


123

in the common carotid artery (CCA). Here, the Reynolds

number of the flow ranges between 485 and 768. Through

the sinus region, the flow in both ICA and ECA changed

significantly and becomes more chaotic with pronounced

vortical coherent structures and strong central vortex

threading being formed through the stenosis. At the ste-

nosed area of the ICA, the Reynolds number can reach as

high as 2100, which indicates the probable transition to

weak turbulent flow. Break-down of vortex structure in the

post-stenotic region can also be observed, which further

ascertains the onset of transitional turbulent flow.

Comparison between experimental and numerical

results

Figure 7 presents the comparison between predicted and

measured stream-wise flow patterns at the center-plane of

the stenosed carotid artery. In general, both measured and

predicted stream-wise flow patterns are in satisfactory

agreement. Flow separations and re-circulation regions due

to the abrupt cross-sectional area expansion at the sinus

region are successfully captured by the CFD simulation,

including the highly skewed velocities caused by curvature

and irregularity of both ICA and ECA. Nevertheless,

stream-wise velocities in both ECA and ICA are found to

be over-predicted by the CFD model.

Figure 8 shows the quantitative comparison of the

measured axial velocity profiles at the center-plane with the

numerical simulation results at four different axial loca-

tions. Axial velocities are plotted against the dimensionless

radial locations and are normalized by the total length of

each cross-section lines. Overall, the predicted velocity

profiles at all cross-section lines are in satisfactory agree-

ment with measurements. In particular, the velocity profiles

along the ICA that are depicted in Fig. 8b–c compared

reasonably well with the experimental data. Nevertheless,

axial velocities are over-predicted around 30–55% in

comparison to the measurements though the main trends

are captured rather well for the ECA. Such error can be

attributed to the probable onset of transitional turbulent

flow in which laminar flow calculations that have been

Fig. 6 Iso-surface plots of vorticity magnitude in a stenosed carotid

bifurcation. The vorticity fields, which are derived from computa-

tional fluid dynamics and particle image velocimetry, are presented in

three dimensional plots and enables the visualisation of rotational

blood flow in the carotid bifurcation. The computationally predicted

results and the experimentally derived flow measurements are

observed to be relatively similar

0.5 m/s

CFD Simulation PIV Measurement

ICA

ECA

ICA

ECA

Flow Separation

Fig. 7 Measured and predicted velocity distribution at the centre-

plane of a stenosed carotid bifurcation. The longitudinal sectioning of

the carotid bifurcation is performed to compare the measured and

predicted flow fields. The PIV and CFD results show good agreement

which highlights the credibility of the numerical simulation in flow

modelling of the patient specific anatomy


123

assumed are invalid. Without an appropriate transitional

model or resolution of all turbulent scales (DNS), the CFD

simulation underestimates the strength of turbulence being

induced through the secondary flows which consequen-

tially over-predicts the axial velocity magnitude.

Nonetheless, it can be noted that the prediction error for the

present investigation is comparable to those that have been

obtained through other researchers [3, 22, 36], who

reported an error ranging from 1 to 47% when compared to

PIV or LDA measurements.

Dimensionless Radial Location [-]

Vel

oci

tyM

agn

itude

(m/s

)

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

CFD ResultsExp. Results


Vel

oci

tyM

agn

itude

(m/s

)

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7



Vel

oci

tyM

agn

itude

(m/s

)

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7



Vel

oci

tyM

agn

itude

(m/s

)

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

CFDResultsExp.Results


Vel

oci

tyM

agn

itude

(m/s

)

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9



Vel

oci

ty M

agn

itu

de

(m/s

)

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7


(a)

(b)

(c)

(d)

ECA ICA

ECA ICA

ECA ICA

ICA

ECA ICA

ECA ICA

CCA

CCA


Vel

oci

tyM

agn

itude

(m/s

)

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7


Fig. 8 Comparison of the measured and predicted local axial velocity

profile. Velocity profiles at selected lines of the stenosed carotid

bifurcation from (a) to (d) are examined using the predicted and

experimental data. Axial velocities are over-predicted around 30–55%

in comparison to the measurements. It may be noted that the results

are relatively accurate for flow in the ECA


123

Figure 9 illustrates the predicted and measured vector

plots of secondary flows at three selected cross-sectional

planes of the stenosed carotid artery. The recirculation

regions of secondary flows are successfully captured by CFD

and the predicted vortex core locations compared favourably

with measurements. Nonetheless, the predicted secondary

flows appear to be relatively weaker when compared to

measurements. This further confirms the breakdown of the

laminar flow calculations in attempting to capture the onset

of turbulence, which subsequently underestimated the

strength of the resultant secondary flows.

Issues during modelling of turbulent transition

In the context of CFD applications, existing uncertainties

and difficulties remain in the modelling of embedded

transition flow. Obviously, the laminar flow assumption

taken in the current CFD simulation is not strictly appli-

cable in attempting to capture the onset of transition tur-

bulent flow. On the other hand, most existing turbulence

models that have been developed primarily for fully tur-

bulent flows cannot be directly applied to capture transition

turbulent flow. Flow in an artery is dominantly laminar

while it may be argued that turbulence can be induced

through the curvatures within the geometry [19]. A number

of numerical studies have also been preformed to investi-

gate the turbulent characteristic of stenotic flows using

different turbulence models [4, 20, 35].

Conclusion

Numerical simulation of an anatomically realistic stenosed

carotid bifurcation is performed and validated against the

experimental results. In general, the main flow character-

istic can be successfully captured using CFD. The predicted

vortex cores of the secondary flow compare favourably with

measurement data. There is a limitation in the capability of

our CFD model to accurately predict the transition of

laminar flow upstream to weak turbulence downstream.

This highlights the deficiency of the current CFD technique

to properly capture transition turbulent flow, and therefore

requires further experimental and numerical studies to

better understand these embedded chaotic flow structures.

Experimental work is currently being carried out to measure

blood flow structure under transient pulsatile flow situation,

which allows additional validation to be performed on the

CFD model in predicting actual blood flow behaviours.

(a)

(b)

(c)

ECA ICA

ECA ICA

CCA

ECA ICA

ECA ICA

CCA

ECA ICA

ECAICA

CCA

CFD Simulation PIV Measurement

0.5 m/s

Vortex core locations Vortex core locations

Vortex core location Vortex core location

Vortex core location Vortex core location

0.5 m/s

0.5 m/s

Fig. 9 Measured and predicted

secondary flow pattern at the

selected planes of a stenosed

carotid bifurcation. Various

sections of the carotid

bifurcation from (a) to (c) are

visualised based on the

predicted and measured flow

(in the top-down direction of the

anatomy). The recirculation

regions of secondary flows and

the predicted vortex core

locations simulated by CFD

agree well with experimental

measurements. The predicted

flow differs slightly from the

measured ones at the common

carotid artery


123

These studies are required to better understand the chaotic

flow structure of transition turbulent, which can prove be

significant in the plaque weakening mechanism.

Acknowledgments The authors thank Dr Hua-Feng Li from RMIT

University for his kind assistance in processing our experimental

results. The financial support provided by the Australian Research

Council (ARC e-research project ID SR0563610 and ARC Discovery

project ID DP0986183) is gratefully acknowledged.

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