Construction and Validation of Subject-Specific Biventricular Finite-Element Models of Healthy and Failing Swine Hearts From High-Resolution DT-MRI

Abstract

Predictive computational modeling has revolutionized classical engineering disciplines and is in the process of transforming cardiovascular research. This is particularly relevant for investigating emergent therapies for heart failure, which remains a leading cause of death globally. The creation of subject-specific biventricular computational cardiac models has been a long-term endeavor within the biomedical engineering community. Using high resolution (0.3 × 0.3 × 0.8 mm) ex vivo data, we constructed a precise fully subject-specific biventricular finite-element model of healthy and failing swine hearts. Each model includes fully subject-specific geometries, myofiber architecture and, in the case of the failing heart, fibrotic tissue distribution. Passive and active material properties are prescribed using hyperelastic strain energy functions that define a nearly incompressible, orthotropic material capable of contractile function. These materials were calibrated using a sophisticated multistep approach to match orthotropic tri-axial shear data as well as subject-specific hemodynamic ventricular targets for pressure and volume to ensure realistic cardiac function. Each mechanically beating heart is coupled with a lumped-parameter representation of the circulatory system, allowing for a closed-loop definition of cardiovascular flow. The circulatory model incorporates unidirectional fluid exchanges driven by pressure gradients of the model, which in turn are driven by the mechanically beating heart. This creates a computationally meaningful representation of the dynamic beating of the heart coupled with the circulatory system. Each model was calibrated using subject-specific experimental data and compared with independent in vivo strain data obtained from echocardiography. Our methods produced highly detailed representations of swine hearts that function mechanically in a remarkably similar manner to the in vivo subject-specific strains on a global and regional comparison. The degree of subject-specificity included in the models represents a milestone for modeling efforts that captures realism of the whole heart. This study establishes a foundation for future computational studies that can apply these validated methods to advance cardiac mechanics research.

A comparison of two quasi‐static computational models for assessment of intra‐myocardial injection as a therapeutic strategy for heart failure

“…The idealized model also captures the twisting motion of the heart, unlike the DTI‐based. The current “gold standard” to validate the model is to compare its strain prediction with in vivo measurement either from tagged magnetic resonance imaging or from echocardiography . The cardiomyocyte stresses predicted by our models are within the range of stresses predicted by validated models.…”

“…The current "gold standard" to validate the model is to compare its strain prediction with in vivo measurement either from tagged magnetic resonance imaging 37 or from echocardiography. 20 The cardiomyocyte stresses predicted by our models are within the range of stresses predicted by validated models. Specifically, volume-averaged stress predictions in both the idealized model (2.84 kPa in ED and 23.4 kPa in ES) and the DTI model (2.53 kPa in ED and 36.08 kPa in ES) are within the ranges of stresses predicted by reported computational models (2.1 ± 4.2 kPa in ED and 23.2 ± 19.8 kPa in ES) that were validated by comparing predicted strains to echocardiographic measurements.…”

“…Specifically, volume-averaged stress predictions in both the idealized model (2.84 kPa in ED and 23.4 kPa in ES) and the DTI model (2.53 kPa in ED and 36.08 kPa in ES) are within the ranges of stresses predicted by reported computational models (2.1 ± 4.2 kPa in ED and 23.2 ± 19.8 kPa in ES) that were validated by comparing predicted strains to echocardiographic measurements. 20 We simulate a myocardial infarct in both models by changing the thickness and stiffness of a section of the LV wall and subsequently simulate two different injection strategies for each in the infarct and the border zone, respectively. We use cardiomyocyte stresses to predict therapy efficacy.…”

A Two-Stage Purkinje Network for More Accurate ECG Representations

“…The cardiovascular system is a multiscale system that operates from the molecular to the cellular to the tissue level to de ning the whole organ (7). Monitoring changes in the smaller levels could reveal how they affect the heart's structure and function and give us the ability to better quantify how heart disease impacts its functionality (8). Thus, with the aim to improve cardiac healthcare, multiscale modeling could be pursued to gain insight into the causes and consequences of CVDs (7,9,10), monitor drug-induced effects on cardiac electromechanics (3,11), optimize treatment options and simulate surgeries to nd the best course of operation (12)(13)(14).…”

Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

“…We previously used tagged magnetic resonance imaging (MRI) to calibrate mechanical properties [4]. Recently, we used ex vivo MRI data to reconstruct an anatomically accurate geometry of the heart with high resolution [13]. However, the material properties were estimated based on global LV metrics, namely stroke volume and longitudinal strain.…”