Including aortic valve morphology in computational fluid dynamics simulations: initial findings and application to aortic coarctation

Abstract

Computational fluid dynamics (CFD) simulations quantifying thoracic aortic flow patterns have not included disturbances from the aortic valve (AoV). 80% of patients with aortic coarctation (CoA) have a bicuspid aortic valve (BAV) which may cause adverse flow patterns contributing to morbidity. Our objectives were to develop a method to account for the AoV in CFD simulations, and quantify its impact on local hemodynamics. The method developed facilitates segmentation of the AoV, spatiotemporal interpolation of segments, and anatomic positioning of segments at the CFD model inlet. The AoV was included in CFD model examples of a normal (tricuspid AoV) and a post-surgical CoA patient (BAV). Velocity, turbulent kinetic energy (TKE), time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) results were compared to equivalent simulations using a plug inlet profile. The plug inlet greatly underestimated TKE for both examples. TAWSS differences extended throughout the thoracic aorta for the CoA BAV, but were limited to the arch for the normal example. OSI differences existed mainly in the ascending aorta for both cases. The impact of AoV can now be included with CFD simulations to identify regions of deleterious hemodynamics thereby advancing simulations of the thoracic aorta one step closer to reality.

Figure 1

Method of patient-specific model construction and valve inclusion. Imaging data, displayed as maximum intensity projections (a1, a3), were used to create 3D CFD models (a2, a4). Temporal PC-MRI magnitude images showing valve leaflets at specific times during systole (b1–b4) were segmented and smoothed with a custom-designed MATLAB program (c1–c4) for example patients having tricuspid (d1) and bicuspid aortic valves (e1). These segmentations were applied to the CFD model inflow to create a time-varying mask of the inflow face for the tricuspid (d2) and bicuspid (e2) valves. Resulting velocity profile assigned to the inflow face using the mask (d3, e3).

Figure 2

Comparison of blood flow velocity waveforms between PC-MRI (solid line), plug (dashed line), and TRI (dotted line) inlet conditions in the AscAo, proximal and distal dAo, IA, LCCA, and LSCA for the normal patient from example 1. Velocity profiles are also presented for 4 time points throughout the cardiac cycle (two during systole as well as in early and late diastole).

Figure 3

Blood flow velocity streamlines at peak systole for simulations run with plug (left column) or valve (right column) inlet conditions. Results from the normal patient in example 1 are shown along the top row while results from the CoA patient with a BAV from example 2 are shown along the bottom row. Inserts reveal velocity profiles and associated vectors in the ascending aorta downstream of the valve leaflets.

Figure 4

Comparison of TAWSS between a plug and tricuspid aortic valve inlet velocity profiles for the normal patient in example 1. Spatial distributions of TAWSS are shown on the vessel (left) and the inserts show the distribution along the anterior wall. Longitudinal and circumferential TAWSS was queried at specific locations to quantify regions of disparity between inlets.

Figure 5

TAWSS differences between plug and TRI for the normal patient in example 1 (left) and plug and BAV for the CoA patient in example 2 (right). Opaque regions reveal the locations where the influence of the inflow waveform was greater than established levels of inter-observer variability. Insets show differences along the AscAo anterior wall.

Figure 6

Comparison of OSI between a plug and tricuspid aortic valve inlet velocity profile for the normal patient in example 1. Spatial distributions of OSI are shown on the vessel (left) and the inserts show the distribution along the anterior wall. Longitudinal and circumferential OSI were queried at specific locations to quantify region of disparity between inlets.

Figure 7

Turbulent kinetic energy (TKE) at peak systole (left column), mid-deceleration (center column), and mid-diastole (right column) for the normal patient from example 1 (top row) and the surgically repaired CoA/BAV patient from example 2 (bottom row). Comparisons were made between the time-varying plug inlet boundary conditions and the respective patient’s AoV. Note: A logarithmic scale was used to visualize the large variation in TKE from different regions of the vessel. Transparent portions of the figure are those portions of the CFD models where TKE values were near zero.

Figure 8

Comparison of blood flow velocity between PC-MRI (solid line), plug (dashed line), and BAV (dotted line) inlet conditions in the AscAo, proximal and distal dAo, innominate artery, LCCA, and LSCA for the surgically repaired CoA/BAV patient from example 2. Velocity profiles are also presented for 4 time points throughout the cardiac cycle (two during systole as well as in early and late diastole).

Figure 9

Comparison of TAWSS between a plug and bicuspid aortic valve inlet velocity profile for the patient with surgically corrected CoA in example 2. Spatial distributions of TAWSS is shown on the vessel (left) and inserts show the distribution along the anterior wall. Longitudinal and circumferential TAWSS was queried at specific locations to quantify regions of disparity between inlets.

Figure 10

Comparison of OSI between a plug and bicuspid aortic valve inlet velocity profile for the CoA patient in example 2. Spatial distributions of OSI are shown on the vessel (left) and the inserts show the distribution along the anterior wall. Longitudinal and circumferential OSI were queried at specific locations to quantify region of disparity between inlets.