Efficient estimation of personalized biventricular mechanical function employing gradient‐based optimization. (22nd April 2018)

• Record Type: Journal Article
• Authors: Finsberg, Henrik; Xi, Ce; Tan, Ju Le; Zhong, Liang; Genet, Martin; Sundnes, Joakim; Lee, Lik Chuan; Wall, Samuel T.
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 34:Number 7(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-04-22
• Subjects: cardiac mechanics -- contractility estimation -- data assimilation -- parameter estimation -- patient specific simulations -- stress estimation; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.2982
• Languages: English
• ISSNs: 2040-7939

Abstract

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. Abstract : 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. By applying the framework using data obtained from 3 healthy human biventricles, we extracted clinically important quantities and explored the role of fiber angles on heart function. Because of its high efficiency, this framework may be applicable to modeling patient‐specific cardiac mechanics for diagnostic purposes.