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Research Papers

Optimization of Cardiovascular Stent Design Using Computational Fluid Dynamics

[+] Author and Article Information
Timothy J. Gundert

 Department of Biomedical Engineering,Marquette University,1515 West Wisconsin Avenue,Milwaukee, WI 53233

Alison L. Marsden, Weiguang Yang

 Mechanical and Aerospace Engineering Department,University of California San Diego,9500 Gillman Drive,La Jolla, CA 92093

John F. LaDisa1

Department of Biomedical Engineering, Marquette University, 1515 West Wisconsin Avenue,Milwaukee, WI 53233; Department of Medicine, Division of Cardiovascular Medicine,  Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226; Department of Pediatrics,Division of Pediatrics,Children’s Hospital of Wisconsin, 9000 W. Wisconsin Avenue, Wauwatosa, WI 53226john.ladisa@marquette.edu

1

Corresponding author.

J Biomech Eng 134(1), 011002 (Feb 09, 2012) (8 pages) doi:10.1115/1.4005542 History: Received June 21, 2011; Revised December 03, 2011; Posted January 23, 2012; Published February 08, 2012; Online February 09, 2012

Coronary stent design affects the spatial distribution of wall shear stress (WSS), which can influence the progression of endothelialization, neointimal hyperplasia, and restenosis. Previous computational fluid dynamics (CFD) studies have only examined a small number of possible geometries to identify stent designs that reduce alterations in near-wall hemodynamics. Based on a previously described framework for optimizing cardiovascular geometries, we developed a methodology that couples CFD and three-dimensional shape-optimization for use in stent design. The optimization procedure was fully-automated, such that solid model construction, anisotropic mesh generation, CFD simulation, and WSS quantification did not require user intervention. We applied the method to determine the optimal number of circumferentially repeating stent cells (NC ) for slotted-tube stents with various diameters and intrastrut areas. Optimal stent designs were defined as those minimizing the area of low intrastrut time-averaged WSS. Interestingly, we determined that the optimal value of NC was dependent on the intrastrut angle with respect to the primary flow direction. Further investigation indicated that stent designs with an intrastrut angle of approximately 40 deg minimized the area of low time-averaged WSS regardless of vessel size or intrastrut area. Future application of this optimization method to commercially available stent designs may lead to stents with superior hemodynamic performance and the potential for improved clinical outcomes.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

(top) Parameterized drawing of a stent cell which is characterized by the cell axial length (la ), circumferential distance between struts (lc ), and intrastrut angle (θ). (bottom) Examples of three stent models with different intrastrut areas, shown to the left of each model.

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Figure 2

Description of the steps necessary for evaluating a stent design. The TAWSS is shown normalized to the average TAWSS in the proximal unstented region of the model.

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Figure 3

Flow chart of the SMF optimization routine. Each bolded box indicates a point in the routine where the cost function for a stent design is evaluated. The optimization stops when the size of the discrete parameter mesh (Δm ) is refined beyond a user specified tolerance (tol).

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Figure 4

The cost function versus the number of repeating circumferential units for stent models with various intrastrut areas in a small vessel (left) and large vessel (right). The intrastrut angle corresponding to the number of repeating units is denoted on the individual plot axes for each design and the optimal design is circled on each plot. Patterns of normalized TAWSSIS are shown for the least, most, and optimal number of circumferential repeating units.

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Figure 5

The cost function versus the intrastrut angle for stent models with various intrastrut areas in a small vessel and large vessel (black lines). The intrastrut angles that correspond to feasible stent designs are shown as vertical lines (gray). The cost function versus the number of circumferentially repeating stent cells is plotted along the gray lines and the number of circumferentially repeating cells is denoted above the lines for models that have been evaluated. Optimal stent designs are circled on all plots.

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Figure 6

Convergence history for the optimization of the intrastrut angle for stent models with various intrastrut areas in a small vessel and large vessel. The Latin hypercube sampling (LHS) portion of the optimization routine is shaded in gray. The surrogate management framework (SMF) represents the portion of the optimization algorithm that used alternating SEARCH and POLL steps to converge on the optimal stent design.

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