Research Papers

A Method for Automatically Optimizing Medical Devices for Treating Heart Failure: Designing Polymeric Injection Patterns

[+] Author and Article Information
Jonathan F. Wenk, Samuel T. Wall, Robert C. Peterson, Sam L. Helgerson

 CardioPolymers, Inc., Laguna Hills, CA 92653

Hani N. Sabbah

 Henry Ford Health System, Detroit, MI 48202

Mike Burger, Nielen Stander

 Livermore Software Technology Corporation, Livermore, CA 94550

Mark B. Ratcliffe

Department of Surgery and San Francisco VA Medical Center, University of California at San Francisco, San Francisco, CA 94121

Julius M. Guccione1

Department of Surgery and San Francisco VA Medical Center, University of California at San Francisco, San Francisco, CA 94121guccionej@surgery.ucsf.edu


Corresponding author.

J Biomech Eng 131(12), 121011 (Nov 24, 2009) (7 pages) doi:10.1115/1.4000165 History: Received May 01, 2009; Revised May 25, 2009; Posted September 04, 2009; Published November 24, 2009; Online November 24, 2009

Heart failure continues to present a significant medical and economic burden throughout the developed world. Novel treatments involving the injection of polymeric materials into the myocardium of the failing left ventricle (LV) are currently being developed, which may reduce elevated myofiber stresses during the cardiac cycle and act to retard the progression of heart failure. A finite element (FE) simulation-based method was developed in this study that can automatically optimize the injection pattern of the polymeric “inclusions” according to a specific objective function, using commercially available software tools. The FE preprocessor TRUEGRID ® was used to create a parametric axisymmetric LV mesh matched to experimentally measured end-diastole and end-systole metrics from dogs with coronary microembolization-induced heart failure. Passive and active myocardial material properties were defined by a pseudo-elastic-strain energy function and a time-varying elastance model of active contraction, respectively, that were implemented in the FE software LS-DYNA . The companion optimization software LS-OPT was used to communicate directly with TRUEGRID ® to determine FE model parameters, such as defining the injection pattern and inclusion characteristics. The optimization resulted in an intuitive optimal injection pattern (i.e., the one with the greatest number of inclusions) when the objective function was weighted to minimize mean end-diastolic and end-systolic myofiber stress and ignore LV stroke volume. In contrast, the optimization resulted in a nonintuitive optimal pattern (i.e., 3 inclusions longitudinally×6 inclusions circumferentially) when both myofiber stress and stroke volume were incorporated into the objective function with different weights.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 7

(a) End-diastolic and (b) end-systolic myofiber stress distribution at midventricle with an injection pattern of 3×10, note that the stress in the inclusion is excluded, (1 hPa=0.1 kPa).

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Figure 8

Response surface plots of the mean (a) end-diastolic and (b) end-systolic myofiber stress within the parameter space of interest at the second iteration. The axes represent the number of inclusions in the longitudinal and circumferential directions. The points are the actual stress values computed from the FE simulations, with the vertical bar through the point representing the error relative to the response surface, (1 hPa=0.1 kPa).

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Figure 1

(a) Reference FE model and (b) modified FE model, with a 2×5 injection pattern, of the dilated LV

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Figure 2

(a) Single inclusion surrounded by local sector region and (b) single circumferential row of 10 inclusions located at midventricle

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Figure 3

Cross sections through inclusions used to explain two-stage mapping. First the fiber orientations are mapped from the reference model (a) to the model with small inclusions (b). In the second stage, the inclusions are enlarged with compensation for volume preservation, with radius=0.35 cm, V=0.18 ml, while the elements keep the properties derived from the first stage.

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Figure 4

LS-DYNA calibration of Mooney–Rivlin parameters using material number 27 least-squares fit option, based on experimental testing of polymeric material. Vertical axis is stress (hPa) and horizontal axis is stretch, (1 hPa=0.1 kPa).

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Figure 5

Example of five injection patterns generated during the optimization: (a) 2×5, (b) 1×1, (c) 3×1, (d) 3×10, and (e) 1×10

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Figure 6

(a) End-diastolic and (b) end-systolic myofiber stress distribution at midventricle with an injection pattern of 1×1, note that the stress in the inclusion is excluded, (1 hPa=0.1 kPa).




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