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Myocardial material parameter estimation from in‐vivo myocardial strain measurements
Author(s) -
Villongco Christopher Tirona,
Covell James W.,
Omens Jeffrey H.,
McCulloch Andrew D.,
Kerckhoffs Roy C. P.
Publication year - 2010
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.24.1_supplement.782.8
Subject(s) - biplane , biomedical engineering , finite element method , perfusion , mathematics , sonomicrometry , convergence (economics) , algorithm , materials science , computer science , cardiology , medicine , physics , hemodynamics , economics , composite material , thermodynamics , economic growth
The aim of this study was to develop and test a new method to estimate myocardial material parameters from 3‐D strains measured in individual canine hearts. The method can also be applied with non‐invasive imaging modalities. Diastolic myocardial 3‐D finite deformations were obtained in open‐chest anesthetized dog hearts by tracking the positions of implanted bead sets in the left ventricular free wall using biplane x‐ray cineradiography. Anatomically detailed finite element models of individual canine hearts were fitted to MR and CT images of perfusion‐fixed hearts. Resting myocardial material parameters were estimated by minimizing the differences between ventricular deformations predicted by the model and those measured in the intact heart using a Levenberg Marquardt nonlinear least‐squares optimization scheme. Artificially generated strains were used to confirm convergence to correct parameters in less than 20 iterations. Noisy artificial data with a realistic measurement error of 0.01 cm resulted in normally distributed parameter solutions, with respective means within 3% of true values. The means of the strains predicted by a model at marker locations with the noisy parameters had an average error of −1.2% ± 4.1% of the true strains. The best estimate of the material properties optimized by the real anatomic model and data are validated by subsequent comparison to experimental and published data.