A method for automatically optimizing medical devices for treating heart failure: designing polymeric injection patterns.

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((R)) 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((R)) 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 longitudinallyx6 inclusions circumferentially) when both myofiber stress and stroke volume were incorporated into the objective function with different weights.

A computationally efficient formal optimization of regional myocardial contractility in a sheep with left ventricular aneurysm.

A non-invasive method for estimating regional myocardial contractility in vivo would be of great value in the design and evaluation of new surgical and medical strategies to treat and/or prevent infarction-induced heart failure. As a first step towards developing such a method, an explicit finite element (FE) model-based formal optimization of regional myocardial contractility in a sheep with left ventricular (LV) aneurysm was performed using tagged magnetic resonance (MR) images and cardiac catheterization pressures. From the tagged MR images, 3-dimensional (3D) myocardial strains, LV volumes and geometry for the animal-specific 3D FE model of the LV were calculated, while the LV pressures provided physiological loading conditions. Active material parameters (T(max_B) and T(max_R)) in the non-infarcted myocardium adjacent to the aneurysm (borderzone) and in myocardium remote from the aneurysm were estimated by minimizing the errors between FE model-predicted and measured systolic strains and LV volumes using the successive response surface method for optimization. The significant depression in optimized T(max_B) relative to T(max_R) was confirmed by direct ex vivo force measurements from skinned fiber preparations. The optimized values of T(max_B) and T(max_R) were not overly sensitive to the passive material parameters specified. The computation time of less than 5 hours associated with our proposed method for estimating regional myocardial contractility in vivo makes it a potentially very useful clinical tool.

Dor procedure for dyskinetic anteroapical myocardial infarction fails to improve contractility in the border zone.

BACKGROUND: Endoventricular patch plasty (Dor) is used to reduce left ventricular volume after myocardial infarction and subsequent left ventricular remodeling. METHODS AND RESULTS: End-diastolic and end-systolic pressure-volume and Starling relationships were measured, and magnetic resonance images with noninvasive tags were used to calculate 3-dimensional myocardial strain in 6 sheep 2 weeks before and 2 and 6 weeks after the Dor procedure. These experimental results were previously reported. The imaging data from 1 sheep were incomplete. Animal specific finite element models were created from the remaining 5 animals using magnetic resonance images and left ventricular pressure obtained at early diastolic filling. Finite element models were optimized with 3-dimensional strain and used to determine systolic material properties, T(max,skinned-fiber), and diastolic and systolic stress in remote myocardium and border zone. Six weeks after the Dor procedure, end-diastolic and end-systolic stress in the border zone were substantially reduced. However, although there was a slight increase in T(max,skinned-fiber) in the border zone near the myocardial infarction at 6 weeks, the change was not significant. CONCLUSIONS: The Dor procedure decreases end-diastolic and end-systolic stress but fails to improve contractility in the infarct border zone. Future work should focus on measures that will enhance border zone function alone or in combination with surgical remodeling.

Inverse parameter fitting of biological tissues: a response surface approach.

In this paper, we present the application of a semi-global inverse method for determining material parameters of biological tissues. The approach is based on the successive response surface method, and is illustrated by fitting constitutive parameters to two nonlinear anisotropic constitutive equations, one for aortic sinus and aortic wall, the other for aortic valve tissue. Material test data for the aortic sinus consisted of two independent orthogonal uniaxial tests. Material test data for the aortic valve was obtained from a dynamic inflation test. In each case, a numerical simulation of the experiment was performed and predictions were compared to the real data. For the uniaxial test simulation, the experimental targets were force at a measured displacement. For the inflation test, the experimental targets were the three-dimensional coordinates of material markers at a given pressure. For both sets of tissues, predictions with converged parameters showed excellent agreement with the data, and we found that the method was able to consistently identify model parameters. We believe the method will find wide application in biomedical material characterization and in diagnostic imaging.