Research Papers

A Computational Method for Predicting Inferior Vena Cava Filter Performance on a Patient-Specific Basis

[+] Author and Article Information
Kenneth I. Aycock

Department of Bioengineering,
Applied Research Laboratory,
The Pennsylvania State University,
University Park, PA 16802
e-mail: aycock@psu.edu

Robert L. Campbell

Department of Mechanical
and Nuclear Engineering,
Applied Research Laboratory,
The Pennsylvania State University,
University Park, PA 16802

Keefe B. Manning

Department of Bioengineering,
The Pennsylvania State University,
University Park, PA 16802
Department of Surgery,
Penn State Hershey Medical Center,
Hershey, PA 17033

Shankar P. Sastry

Scientific Computing and Imaging Institute,
University of Utah,
Salt Lake City, UT 84112

Suzanne M. Shontz

Department of Mathematics and Statistics,
Department of Computer Science
and Engineering,
Center for Computational Sciences,
Graduate Program in Computational Engineering,
Mississippi State University,
Mississippi State, MS 39762

Frank C. Lynch

Department of Surgery,
Penn State Hershey Medical Center,
Hershey, PA 17033

Brent A. Craven

Department of Mechanical
and Nuclear Engineering,
Department of Bioengineering,
Applied Research Laboratory,
The Pennsylvania State University,
University Park, PA 16802
e-mail: craven@psu.edu

Manuscript received November 3, 2013; final manuscript received April 25, 2014; accepted manuscript posted May 8, 2014; published online June 2, 2014. Assoc. Editor: Ender A. Finol.

J Biomech Eng 136(8), 081003 (Jun 02, 2014) (13 pages) Paper No: BIO-13-1517; doi: 10.1115/1.4027612 History: Received November 03, 2013; Revised April 25, 2014; Accepted May 08, 2014

A computational methodology for simulating virtual inferior vena cava (IVC) filter placement and IVC hemodynamics was developed and demonstrated in two patient-specific IVC geometries: a left-sided IVC and an IVC with a retroaortic left renal vein. An inverse analysis was performed to obtain the approximate in vivo stress state for each patient vein using nonlinear finite element analysis (FEA). Contact modeling was then used to simulate IVC filter placement. Contact area, contact normal force, and maximum vein displacements were higher in the retroaortic IVC than in the left-sided IVC (144 mm2, 0.47 N, and 1.49 mm versus 68 mm2, 0.22 N, and 1.01 mm, respectively). Hemodynamics were simulated using computational fluid dynamics (CFD), with four cases for each patient-specific vein: (1) IVC only, (2) IVC with a placed filter, (3) IVC with a placed filter and model embolus, all at resting flow conditions, and (4) IVC with a placed filter and model embolus at exercise flow conditions. Significant hemodynamic differences were observed between the two patient IVCs, with the development of a right-sided jet, larger flow recirculation regions, and lower maximum flow velocities in the left-sided IVC. These results support further investigation of IVC filter placement and hemodynamics on a patient-specific basis.

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Fig. 1

Components used in patient-specific simulations: (a) left-sided IVC, (b) retroaortic IVC, (c) G2 Express IVC filter, and (d) model blood embolus. Posterior and anterior directions are into and out of the page, respectively.

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Fig. 2

FEA meshes: (a) infrarenal IVC solid meshes derived from the full IVC vein surface meshes and (b) G2 express IVC filter mesh (left), with the hook elements which were not included in the contact simulations highlighted at the asterisk (right)

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Fig. 3

(a) Local material coordinates on the retroaortic IVC mesh; (b) plot of average stress σ versus stretch λ for human IVC tissue; material properties assigned to veins using the HGO model: C10 = 0.003, k1 = 1.4, k2 = 100, κ = 0.2, N = 2, and γ = 41.4 deg

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Fig. 4

Iterative procedure for performing the inverse analysis to obtain the approximate zero-load state of the patient veins. The variable x represents the position vector for each node in the vein mesh, where xct is the vein starting geometry from the CT data, xinput is the geometry input at the beginning of each iteration, xdeformed is the geometry resulting from application of BCs in the FEA simulation, and xerror is the difference in node coordinates between the deformed geometry xdeformed and the starting geometry xct. Where present, parenthesis indicate the output of a step.

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Fig. 5

Slices of the fine CFD mesh for the retroaortic case with a placed filter and model embolus: (a) axial plane at maximum embolus diameter and (b) midplane of placed filter

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Fig. 6

Axial distributions of the circumferential average of pressure (top) and WSS (bottom) at different mesh refinements for the retroaortic IVC with a placed IVC filter and a captured embolus

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Fig. 7

Results from the inverse analyses. Note that the geometry in (c) is nearly identical to that in (a), but (c) provides the approximate in vivo stress state.

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Fig. 8

Results of virtual filter and embolus placement in the left-sided ((a) and (b)) and retroaortic ((c) and (d)) veins. (a) and (c) Free, sheathed, and placed IVC filter, from left to right. (b) and (d) Placed IVC filter with model embolus inserted.

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Fig. 9

Axial velocity contours on cross sections of the patient veins starting upstream at (1) and proceeding downstream to (5). Slices are oriented with the posterior and anterior directions up and down on the page, respectively.

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Fig. 10

Axial velocity contours on frontal planes at the midpoint of the IVC filter for the left-sided IVC (top) and the retroaortic IVC (bottom)

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Fig. 11

Circumferential averages of WSS on the left-sided (a) and retroaortic (b) IVCs

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Fig. 12

WSS contours on the patient veins near the filter placement site. Low WSS values occur on the IVC near the IVC filter struts; the highest WSS values are observed near the embolus in the retroaortic IVC.

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Fig. 13

WSS contours on the placed filter and embolus models. High and low WSS values occur on the upstream and downstream portions of the embolus, respectively. In the left-sided IVC, the WSS is highest on the right side of the embolus due to the right-sided jet.



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