In this work we present a methodology to extract

information from medical imaging and use it for hemodynamical

simulation in arteries. Based on in-vivo magnetic resonance images

(MRI), the velocity of the blood flow has been measured at different

positions and times. Also, the anatomy of the vessel has been

converted into a volume mesh suitable for numerical modeling. This

data has been used to solve computationally the dynamics of the fluid

inside the artery in healthy and pathologic cases. As an application,

we have developed a computational model within the carotids. The

next step in the pipeline will be to extend the simulation to fluidstructure

interaction (FSI) to find the parameters in an

atherosclerotic plaque that could lead to rupture.Peer Reviewe

Aris, Ruth

Dyvorne, Hadrien A.

Fayad, Zahi

Houzeaux, G.

Jowkar, M.

Mani, Venkatesh

Santiago, A.

Vázquez, Mariano

English

UPCommons

  
 
 
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From Imaging to Simulation: A framework applied to simulate the 
blood flow in the carotids 
 
R. Arís1, V. Mani2, H. Dyvorne2, M. Jowkar1, A. Santiago1, G. Houzeaux1, Z. Fayad2, M. Vázquez1 
1Barcelona Supercomputing Center (BSC), Spain 
2Translational and Molecular Imaging Institute (TMII), The Mount Sinai Hospital, USA  
 
ruth.aris@bsc.es 
 
 
Abstract-In this work we present a methodology to extract 
information from medical imaging and use it for hemodynamical 
simulation in arteries. Based on in-vivo magnetic resonance images 
(MRI), the velocity of the blood flow has been measured at different 
positions and times. Also, the anatomy of the vessel has been 
converted into a volume mesh suitable for numerical modeling. This 
data has been used to solve computationally the dynamics of the fluid 
inside the artery in healthy and pathologic cases. As an application, 
we have developed a computational model within the carotids. The 
next step in the pipeline will be to extend the simulation to fluid-
structure interaction (FSI) to find the parameters in an 
atherosclerotic plaque that could lead to rupture. 
 
INTRODUCTION 
The complex structure of an atherosclerotic plaque affects the 
dynamics of the fluid inside the vessel. Usually, it is composed 
of different materials such as a lipid pool, calcification or 
hemorrhage. In the last decades, magnetic resonance imaging 
(MRI) has emerged as an imaging modality that enables 
accurate quantification of the main components of the plaque. 
Size, shape and plaque constituents can be identified 
noninvasively [1, 2, 3]. Other MRI techniques like phase-
contrast have also been developed to acquire multidirectional 
blood velocity data in-vivo. 2D and 3D phase-contrast MRI 
has been used for the characterization of normal and 
pathological velocity profiles in those areas [4]. 
To study the dynamics of the flow in the cardiovascular 
system, the simulation tool has been proved to be a powerful 
complement. It has helped to investigate the problem through 
simple and complex geometry models, most of them based on 
in-vivo measurements [5, 6]. Particularly, image-based 
computational modeling can provide valuable information of 
the hemodynamical conditions and can assist the prediction of 
possible outcomes. 
The present work aims to bring imaging and simulation 
closer in a robust framework. In this work, 3D MRI data of 
human carotids is acquired at Mount Sinai Hospital of New 
York. For each case, the geometries of the lumen and the 
vessel wall are segmented from images and converted into a 
computational mesh ready to run the simulations. Velocity 
measurements are also acquired and included as initial 
conditions in the computational model. As a first approach, 
only fluid simulations are performed. Once the framework will 
be validated, it will be extended to FSI to obtain an accurate 
simulation of the phenomenon occurring between the wall and 
the blood flow, to study the effect of the atherosclerotic plaque 
in the carotids. 
METHODOLOGY 
The problem is divided in three different parts: 
 
1. MRI acquisition. Following the study protocols, patients 
are imaged on a Siemens MR/PET (3T) mMR. The imaging 
protocol is defined as follows: (i) the bilateral carotid arteries 
extending 3 cm below and above the carotid bifurcations are 
imaged using an 8-channel carotid coil. (ii) To delineate the 
vessel lumen, localization with gradient echo sequences, time-
of-flight (TOF) images are acquired. (iii) To obtain black 
blood images and measure carotid stenosis, 3D SPACE with 
T2 weighting are acquired in free breathing conditions, 
untriggered with fat suppression. 
   As a result, 2D images in the coronal planes are obtained. 
Each slice has an in-plane resolution of 0.7x0.7 mm2, with an 
slice thickness of 0.7 mm. The flow in a vessel section is 
measured with a 2D, single-slice, cardiac-gated phase contrast 
sequence with through-plane velocity encoding 
(Venc=100cm/s). The acquisition plane is placed 
perpendicular to the carotid artery, 2 cm before the 
bifurcation, using a 3D time of flight acquisition to localize 
the vessels. The slice has the same resolution as the previous 
imaging protocol. 
2. Image analysis and mesh generation. The geometry of the 
carotids is extracted from the MRI raw data performing a 
manual segmentation in ITK-SNAP [7]. The vessel wall is 
reconstructed computationally using the information in three 
different planes in space. In case of plaque, the corresponding 
materials are also identified. Once the segmentation is finished, 
the software generates the surface mesh of the lumen and the vessel 
wall in the STL (STereoLithography) file format. Finally, the mesh 
generator Iris, developed and supported at Barcelona 
Supercomputing Center (BSC), creates the unstructured finite 
element meshes for both fluid and solid geometries (Fig. 1). 
 
3. Simulation. The appropriate framework for the simulation 
of fluid-structure interaction (FSI) in blood vessels is the 
arbitrary Lagrangian-Eulerian (ALE) description of 
continuous media. In this description, the fluid and solid 
domains are allowed to move to follow the distensible vessels 
and deforming fluid domain [8]. In this preliminary work, only 
  
 
 
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fluid simulations are presented. In this case, the fluid is 
simulated as laminar, viscous, incompressible, Newtonian 
flow in a fixed domain solving the Navier-Stokes equations 
and choosing suitable initial and boundary conditions [5]. The 
3D computational model is performed in Alya, the multi-
physics and parallel code developed at BSC [9, 10, 11, 12, 13] 
and solved in parallel in the Spanish Marenostrum 
supercomputer.  
 
 
Fig. 1.  Computational mesh created for a healthy case. Left: meshes of the the 
lumen (grey) and the vessel wall (red), composed of tetrahedral elements. 
Right: view of the bifurcation in the carotids. 
 
Fig. 2 shows a pathological carotid artery where different 
components have been identified. The result of the simulation 
is presented in Fig. 3 and it is compared to the healthy carotid 
artery of the same patient. In both cases, MRI data has been 
used to provide the geometry and boundary conditions (i.e. 
inflow, and outflow) for a computational fluid dynamics 
calculation. 
 
 
 
Fig. 2.  Surface mesh of a pathologic case in the carotids bifurcation. Lumen 
(blue) and wall (brown) are represented in colors. The atherosclerotic plaque 
is drawn in yellow. 
 
 
 
 
 
 
Fig. 3. The image-based computational framework is used to solve the 
hemodynamics of the problem. We compare the dynamics of the fluid in 
healthy and pathological arteries. Colors represent velocities. Right: healthy 
case. The velocity profile is normal, achieving a maximum of 70 cm/s at the 
systolic peak (PSV). Left: pathologic case. Maximum velocities are found in 
the bifurcation. Values are high is this case, achieving 200 cm/s at PSV. 
 
 
VII. FUTURE WORK 
Nowadays, theres a lack of realistic geometries used in 
numerical simulation. The pipeline presented here will 
constitute an efficient bridge to connect image and simulation. 
Given the increasing resolution and accuracy of information 
obtained from clinical imaging, the simulation tool is the 
complement that can provide an efficient way of research, 
prior to clinical trials which are expensive and can be a risk to 
the patients. Simulations can be performed on a patent’s data 
and the results can be delivered to the doctor in an ad-hoc 
visualization manner (virtual lab) to get a deeper insight of the 
problem. 
To identify the wall shear stress in the atherosclerotic material, 
flow will be simulated on a moving mesh using an (ALE) 
strategy. The artery wall and the plaque components will be 
modeled hyperelastic, isotropic, incompressible and 
homogeneous. The coupled FSI model will be solved in 
parallel in Marenostrum. 
 
ACKNOWLEDGMENT 
This work has been written with the support of the 
postdoctoral fellowship grant under the Cardio-Image 
Program, awarded by the Spanish Research Center CNIC.   
 
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From imaging to simulation: a framework applied to simulate the blood flow in the carotids

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From imaging to simulation: a framework applied to simulate the blood flow in the carotids

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