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Research Papers

Evaluating Design of Abdominal Aortic Aneurysm Endografts in a Patient-Specific Model Using Computational Fluid Dynamics

[+] Author and Article Information
Polina A. Segalova1

Department of Mechanical Engineering,  Stanford University, Stanford, CA 94305polina@stanford.edu

Guanglei Xiong

Biomedical Informatics Program,  Stanford University, Stanford, CA 94305

K. T. Venkateswara Rao

Nellix Endovascular, Palo Alto, CA 94303

Christopher K. Zarins

Department of Surgery,  Stanford University, Stanford, CA 94305

Charles A. Taylor

Department of Bioengineering,  Stanford University, Stanford, CA 94305

1

Corresponding author. Present address: James H. Clark Center, E350, 318 Campus Drive, Stanford, CA 94305.

J. Med. Devices 5(4), 041005 (Nov 14, 2011) (8 pages) doi:10.1115/1.4005228 History: Received May 24, 2011; Revised October 01, 2011; Published November 14, 2011; Online November 14, 2011

Computer modeling of blood flow in patient-specific anatomies can be a powerful tool for evaluating the design of implantable medical devices. We assessed three different endograft designs, which are implantable devices commonly used to treat patients with abdominal aortic aneurysms (AAAs). Once implanted, the endograft may shift within the patient’s aorta allowing blood to flow into the aneurismal sac. One potential cause for this movement is the pulsatile force experienced by the endograft over the cardiac cycle. We used contrast-enhanced computed tomography angiography (CTA) data from four patients with diagnosed AAAs to build patient-specific models using 3D segmentation. For each of the four patients, we constructed a baseline model from the patient’s preoperative CTA data. In addition, geometries characterizing three distinct endograft designs were created, differing by where each device bifurcated into two limbs (proximal bifurcation, mid bifurcation, and distal bifurcation). Computational fluid dynamics (CFD) was used to simulate blood flow, utilizing patient-specific boundary conditions. Pressures, flows, and displacement forces on the endograft surface were calculated. The curvature and surface area of each device was quantified for all patients. The magnitude of the total displacement force on each device ranged from 2.43 N to 8.68 N for the four patients examined. Within each of the four patient anatomies, the total displacement force was similar (varying at least by 0.12 N and at most by 1.43 N), although there were some differences in the direction of component forces. Proximal bifurcation and distal bifurcation geometries consistently generated the smallest and largest displacement forces, respectively, with forces observed in the mid bifurcation design falling in between the two devices. The smallest curvature corresponded to the smallest total displacement force, and higher curvature values generally corresponded to higher magnitudes of displacement force. The same trend was seen for the surface area of each device, with lower surface areas resulting in lower displacement forces and vise versa. The patient with the highest blood pressure displayed the highest magnitudes of displacement force. The data indicate that curvature, device surface area, and patient blood pressure impact the magnitude of displacement force acting on the device. Endograft design may influence the displacement force experienced by an implanted endograft, with the proximal bifurcation design showing a small advantage for minimizing the displacement force on endografts.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Volume renderings of the baseline and proximal bifurcation CTA data sets used in this study are depicted. The proximal bifurcation device was anchored in the aneurysm sac with polymer, thus, masking its double lumen design in the figure.

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Figure 2

The workflow for virtual intervention begins by isolating the aneurysm region and creating centerlines. The centerlines are then used to extend tubular endograft geometry and joined with a bifurcated section.

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Figure 3

Four designs (baseline, proximal bifurcation, mid bifurcation, and distal bifurcation) were constructed for each of the four patient anatomies in this study and are pictured above. The three endograft geometries are highlighted in black for each intervention model.

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Figure 4

Pressure waveforms (solid line) at the inlet were calculated for each patient anatomy. The measured systolic (dashed line) and diastolic (dotted line) patient blood pressures are also shown. There was no difference in the inlet blood pressure for a given patient anatomy across the four different models (baseline, proximal bifurcation, mid bifurcation, and distal bifurcation). The maximums and minimums of the pressure waveform (solid line) were within 5% of the measured systolic (dashed line) and diastolic (dotted line) blood pressure values, respectively.

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Figure 5

The total curvature of each device design is plotted versus the total displacement force acting on each device. The three data points for each patient represent the three device designs examined in this study. The correlation coefficient (R2 ) is listed for each patient.

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Figure 6

The total endograft surface area (mm2 ) in all three device designs is plotted versus the total force acting on the endograft. The correlation coefficient (R2 ) is listed for each patient.

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