Research Papers

The Impact of Cardiac Motion on Aortic Valve Flow Used in Computational Simulations of the Thoracic Aorta

[+] Author and Article Information
David C. Wendell

Department of Biomedical Engineering, Marquette University and
Medical College of Wisconsin,
Milwaukee, WI 53233;
Division of Cardiology,
Department of Medicine,
Duke Cardiovascular Magnetic
Resonance Center,
Duke University Medical Center,
Duke Medicine Circle Room 4348,
Durham, NC 27710
e-mail: david.wendell@duke.edu

Margaret M. Samyn, Joseph R. Cava, Mary M. Krolikowski

Department of Pediatrics,
Herma Heart Center,
Children's Hospital of Wisconsin and
the Medical College of Wisconsin,
Milwaukee, WI 53226

John F. LaDisa, Jr.

Department of Biomedical Engineering,
Marquette University and
Medical College of Wisconsin,
Milwaukee, WI 53233;
Division of Cardiovascular Medicine,
Department of Medicine,
Medical College of Wisconsin,
Milwaukee, WI 53226;
Department of Physiology,
Medical College of Wisconsin,
Milwaukee, WI 53226

1Corresponding author.

Manuscript received December 10, 2015; final manuscript received June 12, 2016; published online July 13, 2016. Assoc. Editor: Ender A. Finol.

J Biomech Eng 138(9), 091001 (Jul 13, 2016) (11 pages) Paper No: BIO-15-1637; doi: 10.1115/1.4033964 History: Received December 10, 2015; Revised June 12, 2016

Advancements in image-based computational modeling are producing increasingly more realistic representations of vasculature and hemodynamics, but so far have not compensated for cardiac motion when imposing inflow boundary conditions. The effect of cardiac motion on aortic flow is important when assessing sequelae in this region including coarctation of the aorta (CoA) or regurgitant fraction. The objective of this investigation was to develop a method to assess and correct for the influence of cardiac motion on blood flow measurements through the aortic valve (AoV) and to determine its impact on patient-specific local hemodynamics quantified by computational fluid dynamics (CFD). A motion-compensated inflow waveform was imposed into the CFD model of a patient with repaired CoA that accounted for the distance traveled by the basal plane during the cardiac cycle. Time-averaged wall shear stress (TAWSS) and turbulent kinetic energy (TKE) values were compared with CFD results of the same patient using the original waveform. Cardiac motion resulted in underestimation of flow during systole and overestimation during diastole. Influences of inflow waveforms on TAWSS were greatest along the outer wall of the ascending aorta (AscAo) (∼30 dyn/cm2). Differences in TAWSS were more pronounced than those from the model creation or mesh dependence aspects of CFD. TKE was slightly higher for the motion-compensated waveform throughout the aortic arch. These results suggest that accounting for cardiac motion when quantifying blood flow through the AoV can lead to different conclusions for hemodynamic indices, which may be important if these results are ultimately used to predict patient outcomes.

Copyright © 2016 by ASME
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Grahic Jump Location
Fig. 1

Valve tracking at four time points throughout the cardiac cycle. A line delineates landmarks placed on the LV wall and ventricular septum. These locations were tracked in consecutive images to account for the basal plane motion in the AoV PC-MRI measurements. Dashed lines show the original basal plane location.

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Fig. 2

Method of 3D patient-specific model construction. Imaging data, thresholded to isolate the vascular region of interest, were segmented using a procedure of placing spheres throughout the region of interest (a). These spheres were expanded to fill the lumen (b) and converted to a 3D model (c). The isolated 3D model (d) was then smoothed (e) and its centerline was extracted (f). The centerline and 3D model were then used to extract segments at discrete increments throughout the vessel (g), and these segments were lofted into a solid model (h), and discretized into a finite-element mesh (i).

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Fig. 3

PC-MRI measurements acquired in the AscAo and AoV showing the disparity between quantification at different locations (a). The correction technique (represented as the basal component of aortic flow) was used to compensate for the motion of the heart, eliminating the majority of diastolic flow (b).

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Fig. 4

Through-plane velocity profiles extracted from the PC-MRI data (top row), CFD simulations using the original (i.e., uncorrected) AoV flow (center row), and the motion-compensated (i.e., corrected) AoV flow (bottom row) at three time points throughout the cardiac cycle including the acceleration phase of systole (t1; left column), near peak systole (t2; center column), and the deceleration phase (t3; right column)

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Fig. 5

Blood flow velocity streamlines at peak systole for the uncorrected (left) and corrected (right) inlet boundary conditions as viewed from the left side (top) and anterior (bottom) view of the arch. Respective images show areas of recirculation surrounding the inflow jet and in the coronary sinuses and elevated velocity resulting from thoracic geometry through the transverse arch and dAo.

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Fig. 6

Comparison of TAWSS between the uncorrected and corrected inlet profiles. TAWSS is shown on the vessel (left), and the insets show the distribution along the anterior wall of the vessel. The aortic arch was unwrapped to visualize TAWSS and queried longitudinally and circumferentially at discrete locations to quantify region of largest disparity between inlets.

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Fig. 7

TAWSS differences between corrected and uncorrected inflow waveforms. Data were thresholded to accentuate regions of the vessel exhibiting >13% difference between simulation results, which a prior study identified as the interobserver variability in WSS from their CFD model building process. Insets show areas of particular interest in the AscAo.

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Fig. 8

Cumulative percentage distribution of the total area exposed to values of TAWSS from 0 to 200 dyn/cm2 for simulations using corrected and uncorrected AoV PC-MRI measurements as model inputs (left). Differences in the total area at TAWSS values from approximately 5–150 dyn/cm2 are more pronounced for uncorrected versus corrected simulations than for successive mesh densities (2.3 × 106 versus 3.2 × 106 elements). TAWSS values from 0 to 50 dyn/cm2 are highlighted in the histogram (2 dyn/cm2 bins) for values with in this range (right). A more dense mesh and correcting for cardiac motion both shift TAWSS from lower potentially adverse values to higher values traditionally through to be protective. The collective figure also highlights the importance of correcting for cardiac motion relative to aspects of computational modeling, such as mesh dependence, that are known to be of key importance for CFD simulations.

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Fig. 9

TKE at peak systole (left column), mid-deceleration (center column), and mid-diastole (right column) for each inflow waveform (uncorrected: left and corrected: right). Comparisons were made between inlet boundary conditions at each time point.




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