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Research Papers

Computational Simulations for Aortic Coarctation: Representative Results From a Sampling of Patients

[+] Author and Article Information
John F. LaDisa1 n2

Department of Biomedical Engineering,  Marquette University, Milwaukee, WI 53233; Department of Pediatrics, Children’s Hospital of Wisconsin, Milwaukee, WI 53226 e-mail: john.ladisa@mu.edu

C. Alberto Figueroa1

Department of Bioengineering,  Stanford University, Stanford, CA 94305 INRIA Paris-Rocquencourt BP 105, 78153 Le Chesnay Cedex, France Aerospace Engineering Sciences, University of Colorado at Boulder, Boulder, CO 80309Department of Bioengineering,  Stanford University, Stanford, CA 94305Department of Biomedical Engineering,  Marquette University, Milwaukee, WI 53233Department of Radiology,  Stanford University, Stanford, CA 94305Department of Bioengineering, Department of Pediatrics,  Stanford University, Stanford, CA 94305; Lucile Packard Children’s Hospital, Palo Alto, CA 94304Department of Bioengineering, Department of Radiology,  Stanford University, Stanford, CA 94305

Irene E. Vignon-Clementel, Hyun Jin Kim, Nan Xiao, Laura M. Ellwein, Frandics P. Chan, Jeffrey A. Feinstein, Charles A. Taylor

Department of Bioengineering,  Stanford University, Stanford, CA 94305 INRIA Paris-Rocquencourt BP 105, 78153 Le Chesnay Cedex, France Aerospace Engineering Sciences, University of Colorado at Boulder, Boulder, CO 80309Department of Bioengineering,  Stanford University, Stanford, CA 94305Department of Biomedical Engineering,  Marquette University, Milwaukee, WI 53233Department of Radiology,  Stanford University, Stanford, CA 94305Department of Bioengineering, Department of Pediatrics,  Stanford University, Stanford, CA 94305; Lucile Packard Children’s Hospital, Palo Alto, CA 94304Department of Bioengineering, Department of Radiology,  Stanford University, Stanford, CA 94305

1

These authors contributed equally to this work.

2

Corresponding author.

J Biomech Eng 133(9), 091008 (Oct 14, 2011) (9 pages) doi:10.1115/1.4004996 History: Received July 14, 2011; Revised August 31, 2011; Published October 14, 2011

Treatments for coarctation of the aorta (CoA) can alleviate blood pressure (BP) gradients (Δ), but long-term morbidity still exists that can be explained by altered indices of hemodynamics and biomechanics. We introduce a technique to increase our understanding of these indices for CoA under resting and nonresting conditions, quantify their contribution to morbidity, and evaluate treatment options. Patient-specific computational fluid dynamics (CFD) models were created from imaging and BP data for one normal and four CoA patients (moderate native CoA: Δ12 mmHg, severe native CoA: Δ25 mmHg and postoperative end-to-end and end-to-side patients: Δ0 mmHg). Simulations incorporated vessel deformation, downstream vascular resistance and compliance. Indices including cyclic strain, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were quantified. Simulations replicated resting BP and blood flow data. BP during simulated exercise for the normal patient matched reported values. Greatest exercise-induced increases in systolic BP and mean and peak ΔBP occurred for the moderate native CoA patient (SBP: 115 to 154 mmHg; mean and peak ΔBP: 31 and 73 mmHg). Cyclic strain was elevated proximal to the coarctation for native CoA patients, but reduced throughout the aorta after treatment. A greater percentage of vessels was exposed to subnormal TAWSS or elevated OSI for CoA patients. Local patterns of these indices reported to correlate with atherosclerosis in normal patients were accentuated by CoA. These results apply CFD to a range of CoA patients for the first time and provide the foundation for future progress in this area.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Computational models of the patients analyzed and their inflow waveforms (rest = solid lines; simulated exercise = dashed lines). Lines on each model indicate where cyclic strain calculations were performed and the approximate center of 5 mm bands used to quantify WSS indices. Regions of the ascending aorta (AscAo), transverse arch (TA), and descending aorta (dAo) are also depicted.

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Figure 2

Volume-rendered velocity during peak systole (top row), mid-to-late systole (middle row) and end diastole (bottom row) under resting and simulated moderate exercise conditions. Note the difference in scale for end diastole.

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Figure 3

Color contours of systolic wall displacement mapped to the reference diastolic configuration and examples of associated cyclic strain plots in the ascending aorta during rest (solid lines) and simulated moderate exercise (dashed lines)

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Figure 4

Distributions of time-averaged wall shear stress under resting (first row) and simulated moderate exercise (second row) conditions. Distributions of oscillatory shear index under resting (third row) and simulated moderate exercise (fourth row) conditions.

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Figure 5

TAWSS results were unwrapped about longitudinal axes along the inner curvature of the aorta, and anterior surface of the head and neck arteries (shown in black on the left-most side). Unwrapped plots are provided for the aorta and innominate, right (RCCA) and left carotid (LCCA), and left subclavian (LSA) arteries. Circumferential TAWSS plots in the descending aorta and in vessels one diameter distal to their branching from the aorta are also provided for the patient with severe native CoA (empty circles) as compared to the normal patient (solid circles).

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