0
Research Papers

Design Optimization of Vena Cava Filters: An Application to Dual Filtration Devices

[+] Author and Article Information
Michael A. Singer1

Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94551msinger2006@gmail.com

Stephen L. Wang

Division of Vascular and Interventional Radiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA 95051

Darin P. Diachin

 Kanoga Technologies, Livermore, CA 94551

1

Corresponding author.

J Biomech Eng 132(10), 101006 (Oct 01, 2010) (10 pages) doi:10.1115/1.4002488 History: Received December 06, 2009; Revised August 14, 2010; Posted September 01, 2010; Published October 01, 2010; Online October 01, 2010

Pulmonary embolism (PE) is a significant medical problem that results in over 300,000 fatalities per year. A common preventative treatment for PE is the insertion of a metallic filter into the inferior vena cava that traps thrombi before they reach the lungs. The goal of this work is to use methods of mathematical modeling and design optimization to determine the configuration of trapped thrombi that minimizes the hemodynamic disruption. The resulting configuration has implications for constructing an optimally designed vena cava filter. Computational fluid dynamics is coupled with a nonlinear optimization algorithm to determine the optimal configuration of a trapped model thrombus in the inferior vena cava. The location and shape of the thrombus are parametrized, and an objective function, based on wall shear stresses, determines the worthiness of a given configuration. The methods are fully automated and demonstrate the capabilities of a design optimization framework that is broadly applicable. Changes to thrombus location and shape alter the velocity contours and wall shear stress profiles significantly. For vena cava filters that trap two thrombi simultaneously, the undesirable flow dynamics past one thrombus can be mitigated by leveraging the flow past the other thrombus. Streamlining the shape of the thrombus trapped along the cava wall reduces the disruption to the flow but increases the area exposed to low wall shear stress. Computer-based design optimization is a useful tool for developing vena cava filters. Characterizing and parametrizing the design requirements and constraints is essential for constructing devices that address clinical complications. In addition, formulating a well-defined objective function that quantifies clinical risks and benefits is needed for designing devices that are clinically viable.

FIGURES IN THIS ARTICLE
<>
Copyright © 2010 by American Society of Mechanical EngineersThe United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes.
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 2

Results of the grid refinement study: (a) contour plots of the normalized axial and transverse components of velocity using the medium grid resolution; (b) convergence of the normalized WSS along the right (top) and left (bottom) walls of the IVC. Gaps in the WSS profiles indicate the locations of renal inflow.

Grahic Jump Location
Figure 3

Normalized contours of the axial velocity for the initial and final configurations of the verification test cases: (a) one parameter problem, (b) two parameter problem, (c) three parameter problem, and (d) baseline solution

Grahic Jump Location
Figure 4

Normalized WSS profiles for the starting one, two, and three parameter configurations and for the baseline configuration of the verification test problems: top figure is WSS along the right cava wall; bottom figure is WSS along the left cava wall. Gaps in the WSS profiles indicate the locations of renal inflow.

Grahic Jump Location
Figure 5

Contours of the normalized axial component of velocity for the baseline and optimal configurations with one design parameter: (a) no (left), 0.5 ml (center), and 1 ml (right) thrombi upstream and 1 ml thrombus downstream; (b) no (left), 0.5 ml (center), and 1 ml (right) thrombi upstream and 2 ml thrombus downstream

Grahic Jump Location
Figure 6

Normalized WSS profiles for the optimal configurations with one design parameter: (a) 1 ml thrombus downstream; (b) 2 ml thrombus downstream. Wall shear stresses along the right and left walls of the vena cava are shown in the top and bottom figures, respectively. Gaps in the WSS profiles indicate the locations of renal inflow.

Grahic Jump Location
Figure 7

Flow characteristics for the optimal configuration with two design parameters: (a) normalized axial component of velocity with 0.5 ml upstream thrombus and 1 ml (left) and 2 ml (right) downstream thrombi; (b) normalized WSS along the right (top) and left (bottom) walls of the vena cava. Gaps in the WSS profiles indicate the locations of renal inflow.

Grahic Jump Location
Figure 1

Two-dimensional computational model: (a) schematic of the flow configuration; (b) ten overlapping grids used to discretize the geometry, including the higher resolution grids near the two model thrombus

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In