INTRODUCTION
Surgical procedures have confirmed that plaque build-up in blood vessels does not randomly occur. Instead, it forms and progresses in specific regions where blood flow is highly disturbed (VanderLaan et al., 2004). In this work, we studied the effect of geometrically-induce hemodynamics on mass transport in carotid arteries; and to determine its use in plaque progression modelling. Our results show that the flow is able to move mass particles from main flow towards outer walls of the external and internal carotid arteries via vortex recirculation and shedding at the close of peak flow. These phenomena will play a role, but not alone, in development of plaque that can lead to stenosis and increased workload on major organs through reflected pressure waves.
METHODOLOGY
First, we apply level set segmentation to the acquired MRI dataset to obtain the vessel of interest. Next, we convert the 3D surface model to a solid step file and export it to ANSYS Workbench for remeshing, modelling and simulation. 2D models (as shown in figure 1 right) were obtained from the contour of the 3D model and exported with 0.5mm thickness. We discretise all geometries with a body sizing of 0.05mm, 15 layers of inflation and total thickness of 0.64mm. Finally, we use a periodic inflow velocity of 0.5 m/s at peak amplitude and 0.1 m/s at minimum from the work of Sinnott et al. (2015). The fluid was modelled as non-Newtonian using a user-defined function in four simulated models as shown in figure (2).
RESULTS
The results show an increase in total pressure of 1.34x104 Pascals at the bifurcation stagnant region. WSS ranged from 0.022x10-3 to 1.5 Pascals except at inner walls of the ICA and ECA as expected based on curvature flow with corresponding velocities in ranges of 0.001 to 1ms-1. The results obtained in original geometry agreed with the work of others (Groen et al., 2007; Bahrami and Norouzi 2018) and similar case studies of wall shear stress and pressure distributions. Table (1) shows a sample data frame of time-series data from results taken from all case studies,. Four attributes (flow velocity, plaque thickness, class labels and response variables) were developed based on feature engineering to reduce multicollinearity among predictor variables. Velocities were measured between 0.001-0.26 (normal), 0.44 (mild), 0.64 (moderate) and 1ms-1 (severe). In total, 361 datapoints were measured, randomised and labelled; while a test sample of 57 were removed as a test set for model scoring and selection using evaluation metrics.
Table 1: CFD Measures attributes
Velocity (m/s)
|
Plaque Thickness
|
Class_n
|
Response
|
0.199019
|
1.055547
|
0
|
Normal
|
0.282149
|
1.352204
|
1
|
Mild
|
0.187053
|
0.765789
|
0
|
Normal
|
0.621982
|
3.509114
|
2
|
Moderate
|
0.295608
|
1.476386
|
1
|
Mild
|
0.978112
|
4.94
|
3
|
Severe
|
0.97964
|
5.15
|
3
|
Severe
|