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Int J Numer Method Biomed Eng. 2018 Jul;34(7):e2982. doi: 10.1002/cnm.2982. Epub 2018 Apr 22.

Efficient estimation of personalized biventricular mechanical function employing gradient-based optimization.

Finsberg H1,2,3, Xi C4, Tan JL5, Zhong L5,6, Genet M7,8, Sundnes J1,2,3, Lee LC4, Wall ST1,2,9.

Author information

1
Simula Research Laboratory, 1325, Lysaker, Norway.
2
Center for Cardiological Innovation, Songsvannsveien 9, 0372, Oslo, Norway.
3
Department of Informatics, University of Oslo, P.O. Box 1080, Blindern, 0316 Oslo, Norway.
4
Department of Mechanical Engineering, Michigan State University, 220 Trowbridge Rd, East Lansing, 48824, MI, USA.
5
National Heart Center Singapore, 5 Hospital Dr, Singapore.
6
Duke National University of Singapore, 8 College Road, Singapore.
7
Mechanics Department and Solid Mechanics Laboratory, École Polytechnique (CNRS, Paris-Saclay University), Route de Saclay, 91128, Palaiseau, France.
8
M3DISIM research team, INRIA (Paris-Saclay University), 91120, Palaiseau, France.
9
Department of Mathematical Science and Technology, Norwegian University of Life Sciences, Universitetstunet 3 1430 Ås, Norway.

Abstract

Individually personalized computational models of heart mechanics can be used to estimate important physiological and clinically-relevant quantities that are difficult, if not impossible, to directly measure in the beating heart. Here, we present a novel and efficient framework for creating patient-specific biventricular models using a gradient-based data assimilation method for evaluating regional myocardial contractility and estimating myofiber stress. These simulations can be performed on a regular laptop in less than 2 h and produce excellent fit between measured and simulated volume and strain data through the entire cardiac cycle. By applying the framework using data obtained from 3 healthy human biventricles, we extracted clinically important quantities as well as explored the role of fiber angles on heart function. Our results show that steep fiber angles at the endocardium and epicardium are required to produce simulated motion compatible with measured strain and volume data. We also find that the contraction and subsequent systolic stresses in the right ventricle are significantly lower than that in the left ventricle. Variability of the estimated quantities with respect to both patient data and modeling choices are also found to be low. Because of its high efficiency, this framework may be applicable to modeling of patient specific cardiac mechanics for diagnostic purposes.

KEYWORDS:

cardiac mechanics; contractility estimation; data assimilation; parameter estimation; patient specific simulations; stress estimation

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