Aortofemoral Normal - 1

Clinical Significance

Studying the hemodynamics of the vasculature distal to the abdominal aorta may be important in understanding common diseases in peripheral arteries downstream of the thoracic aorta. Diseases in the peripheral vasculature affect millions of people in the U.S and can have a profound effect on daily quality of life.

Peripheral arterial disease is the build-up of fatty tissue, or atherosclerosis, in lower extremity arteries. By 2001 at least 10 million people in the U.S were estimated to have peripheral arterial disease. The prevalence of peripheral arterial occlusive disease increases with age and can increase to up to 20% of the population in the geriatric population. Up to 4 million people in the U.S suffer from intermittent claudication causing pain in the legs during exercise. Atherosclerotic occlusive disease of the lower extremity arteries is a major cause of walking impairment, pain, ulcerations and gangrene.

Renal artery stenosis can have a prevalence of up to 45% in selective populations, specifically populations with other vascular disease. Prevalence can be from 1-6% in hypertensive patients to 30-45% in patients with aortoiliac occlusive disease or abdominal aortic aneurysms. It is most often caused by atherosclerosis in the renal arteries and is often undetected until symptoms become severe. The most common symptom of renal artery stenosis is hypertension, which can have significant effects on the entire vasculature. Up to 24% of patients with renal insufficiency, which can lead to end-stage renal disease renal disease, had renal artery stenosis, suggesting that renal artery stenosis may play an important role in kidney failure.

Clinical Data

Patient-specific volumetric image data was obtained to create physiological models and blood flow simulations. The RAS coordinate system was assumed for the image data orientation. Voxel Spacing, voxel dimensions, and physical dimensions are provided in the Right-Left (R ), Anterior-Posterior (A), and Superior-Inferior (S) direction. The patient was 67 years old and male. Details of the image data are listed as below:

Volumetric image data details (MR)

R A S
Voxel Spacing (mm) 0.7813 2.0000 0.7813
Voxel Dimensions 512 64 512
Physical Dimensions (mm) 400 128 400

Coronal MIP image:

3-D Volume Rendering:

Model Description

The aortofemoral model extends from the supraceliac aorta to the femoral and profunda femoris artery bifurcation. Using Simvascular and the image data above, the geometrical models are generated by selecting centerline paths along the vessels, creating 2D segmentations along each of these paths, and then lofting the segmentations together to create a solid model. A separate solid model was created for each vessel and Boolean addition was used to generate a single model representing the complete aortofemoral model. The vessel junctions were then blended to create a smoothed model.

Geometric model details

Number of inlets Number of outlets Volume(cm3) Surface Area(cm2) Number of Vessel Paths Number of 2-D Segmentations
1 19 182.581 548.575 19 300

Physiological Assumption

Blood Viscosity Blood Density
0.04 g/cm•s2 1.06 g/cm3

Vessel Paths:

Vessel Segmentations:

Vessel Geometric Model:

Boundary Conditions

Inlet Boundary Conditions

A supraceliac aorta blood flow waveform derived from PC-MRI data was prescribed to the inlet of the computational fluid dynamics (CFD) model. Note that the cardiac output is not the same as the supraceliac flow, or the flow prescribed at the inlet. The flow to the supraceliac aorta from PC-MRI was 4.04 L/min.

Flow Waveform:

Period and Cardiac Output:

Period (s) Cardiac Output (L/min) Profile Type
0.75 6.12 Womersley

Outlet Boundary Conditions

In order to represent the effects of vessels distal to the CFD model, a three-element Windkessel model can be applied at each outlet . This model consists of proximal resistance (Rp), capacitance (C ), and distal resistance (Rd) representing the resistance of the proximal vessels, the capacitance of the proximal vessels, and the resistance of the distal vessels downstream of each outlet, respectively.

Windkessel Model:

These parameters are defined using the mean flow to each outlet calculated from PC-MRI. RCR values are shown on the table.

RCR Values for Each Outlet:

Face NameRpCRd
splenic_br1 2096.020.000026045005.00
splenic_br2 2095.960.000026044904.30
hepatic_br1 1511.530.000036032383.60
hepatic_br2 2095.590.000026045243.00
SMA 867.840.000062718429.90
SMA_br1 1617.350.000033735259.30
R_sup_renal 609.700.000089312991.00
R_inf_renal 2222.740.000024547359.60
L_sup_renal 1330.800.000040827260.40
L_inf_renal 829.380.000065617593.90
IMA 1583.130.000034434215.40
R_ext_iliac 718.270.000076015945.20
R_int_iliac 3169.510.000017270363.10
R_int_iliac_br1 7094.640.0000077157504.00
R_int_iliac_br2 7130.800.0000076158139.00
L_ext_iliac 718.270.000076015945.20
L_int_iliac 3903.050.000014086649.70
L_int_iliac_br1 4424.330.000012398323.60
L_int_iliac_br2 8737.840.0000062193983.00

Simulation & Results

Conservation of mass and Navier-Stokes equations were solved using 3D finite element methods assuming rigid and non-slip walls. The number of time steps per cycle is 3200 with fixed time step size. The simulation was run in cgs units for several cardiac cycles to allow the flow rate and pressure fields to stabilize. Simulation results were quantified for the last cardiac cycle. Paraview, an open-source scientific visualization application, was used to visualize the results. A volume rendering of velocity magnitude for three time points during the cardiac cycle can be seen.

(a)Peak Systole; (b)End Systole; (\(c\))End Diastole

Surface distribution of time-averaged blood pressure (TABP), time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI) were also visualized and can be seen.

(a)TABP; (b)TAWSS; (\(c\))OSI