Relationship of Transmural Variations in Myofiber Contractility to Left Ventricular Ejection Fraction: Implications for Modeling Heart Failure Phenotype With Preserved Ejection Fraction

Bi-ventricular elastic material parameters estimation using 3D CMR myocardial strains in rheumatic heart disease patients

Understanding the elastic material behavior of myocardium during the diastolic phase is critical for evaluating cardiac function and improving treatments for diastolic abnormalities. This study introduces a novel multi-objective optimization framework that incorporates both strain and volume measurements to enhance the accuracy of myocardial property assessments in Rheumatic Heart Disease (RHD) patients and healthy controls. By employing global volume and strain measurements instead of segmented strains from the sixteen AHA regions, we achieve a robust alignment with the Klotz curve across all groups, indicating an accurate simulation of end-diastolic pressure-volume relationships (EDPVRs). Our approach uniquely integrates combinations of longitudinal, circumferential, and radial strains, resulting in an unprecedented reduction in errors between clinical and simulated strain values, with less than one percent difference for targeted parameters. The results demonstrate that the alignment between computational predictions and clinical measurements depends significantly on the choice of optimization target. The study reveals significant differences in tissue mechanics between RHD patients and healthy controls, with notable variations in ventricular stiffness and fiber orientations across optimization targets, confirmed through rigorous statistical analyses. The observed variations in fiber angles, particularly the smaller angles for longitudinal strains and steeper angles for circumferential strains, underscore the intricate relationship between myocardial fiber architecture and cardiac deformation, offering deeper insights into ventricular biomechanics. By presenting qualitative and quantitative differences in stress and strain distributions, this research advances the understanding of myocardial mechanics, highlighting the clinical relevance of fiber orientation and material properties in modeling cardiac mechanics and distinguishing diseased from healthy myocardial behavior.

Physical model of end-diastolic and end-systolic pressure-volume relationships of a heart

Left ventricular stiffness and contractility, characterized by the end-diastolic pressure-volume relationship (EDPVR) and the end-systolic pressure-volume relationship (ESPVR), are two important indicators of the performance of the human heart. Although much research has been conducted on EDPVR and ESPVR, no model with physically interpretable parameters combining both relationships has been presented, thereby impairing the understanding of cardiac physiology and pathology. Here, we present a model that evaluates both EDPVR and ESPVR with physical interpretations of the parameters in a unified framework. Our physics-based model fits the available experimental data and in silico results very well and outperforms existing models. With prescribed parameters, the new model is used to predict the pressure-volume relationships of the left ventricle. Our model provides a deeper understanding of cardiac mechanics and thus will have applications in cardiac research and clinical medicine.

Modelling cardiac mechanics in doxorubicin-induced cardiotoxicity following childhood acute lymphoblastic leukemia using a combination of cardiac magnetic resonance imaging, cardiopulmonary exercise testing and the CircAdapt model

Children with acute lymphoblastic leukemia (ALL) are treated with doxorubicin-based chemotherapy that can lead to cardiotoxicity which is a well-known cause of mortality. This study aims to characterize myocardial subtle changes induced by doxorubicin-related cardiotoxicity. We used the combination of cardiac magnetic resonance (CMR) imaging, cardiopulmonary exercise testing and the CircAdapt model to explore hemodynamics and intraventricular mechanisms at rest and during exercise in 53 childhood ALL survivors. A sensitivity analysis of the CircAdapt model identified the most influencing parameters on the left ventricle volume. ANOVA were performed to explore significant differences between left ventricle stiffness, contractility, and arteriovenous pressure drop, as well as survivors' prognostic risk groups. No significant differences were observed between prognostic risk groups. The left ventricle stiffness and left ventricle contractility were non-significantly higher in survivors receiving cardioprotective agents (94.3 %), compared to those at standard and high prognostic risk (77 % and 86 %, respectively). In both left ventricle stiffness and left ventricle contractility, we observed that survivors receiving cardioprotective agents were close to the nominal value of CircAdapt (healthy reference group value is 100 %). This study allowed to improve our knowledge of potential subtle myocardial changes induced by doxorubicin-related cardiotoxicity in childhood ALL survivors. This study confirms that survivors exposed to a high cumulative dose of doxorubicin during treatments are at potential risk of myocardial changes many years after the end of their cancer, while cardio-protective agents may prevent changes in cardiac mechanical properties.

Material property alterations for phenotypes of heart failure with preserved ejection fraction: A numerical study of subject-specific porcine models

A substantial proportion of heart failure patients have a preserved left ventricular (LV) ejection fraction (HFpEF). This condition carries a high burden of morbidity and mortality and has limited therapeutic options. left ventricular pressure overload leads to an increase in myocardial collagen content, causing left ventricular stiffening that contributes to the development of heart failure patients have a preserved left ventricular ejection fraction. Although several heart failure patients have a preserved left ventricular ejection fraction models have been developed in recent years to aid the investigation of mechanical alterations, none has investigated different phenotypes of the disease and evaluated the alterations in material properties. In this study, two similar healthy swine were subjected to progressive and prolonged pressure overload to induce diastolic heart failure characteristics, providing a preclinical model of heart failure patients have a preserved left ventricular ejection fraction. Cardiac magnetic resonance imaging (cMRI) scans and intracardiac pressures were recorded before and after induction. In both healthy and disease states, a corresponding finite element (FE) cardiac model was developed via mesh morphing of the Living Heart Porcine model. The material properties were derived by calibrating to its passive and active behavior. The change in the passive behavior was predominantly isotropic when comparing the geometries before and after induction. Myocardial thickening allowed for a steady transition in the passive properties while maintaining tissue incompressibility. This study highlights the importance of hypertrophy as an initial compensatory response and might also pave the way for assessing disease severity.

A Computationally Efficient Approach to Simulate Heart Rate Effects Using a Whole Human Heart Model

Computational modeling of the whole human heart has become a valuable tool to evaluate medical devices such as leadless pacemakers, annuloplasty rings and left ventricular assist devices, since it is often difficult to replicate the complex dynamic interactions between the device and human heart in bench-top and animal tests. The Dassault Systèmes Living Heart Human Model (LHHM) is a finite-element model of whole-human-heart electromechanics that has input parameters that were previously calibrated to generate physiological responses in a healthy heart beating at 60 beat/min (resting state). This study demonstrates that, by adjusting only six physiologically meaningful parameters, the LHHM can be recalibrated to generate physiological responses in a healthy heart beating at heart rates ranging from 90−160 beat/min. These parameters are as follows: the sinoatrial node firing period decreases from 0.67 s at 90 bpm to 0.38 s at 160 bpm, atrioventricular delay decreases from 0.122 s at 90 bpm to 0.057 s at 160 bpm, preload increases 3-fold from 90 bpm to 160 bpm, body resistance at 160 bpm is 80% of that at 90 bpm, arterial stiffness at 160 bpm is 3.9 times that at 90 bpm, and a parameter relating myofiber twitch force duration and sarcomere length decreases from 238 ms/mm at 90 bpm to 175 ms/mm at 160 bpm. In addition, this study demonstrates the feasibility of using the LHHM to conduct clinical investigations in AV delay optimization and hemodynamic differences between pacing and exercise. AV delays in the ranges of 40 ms to 250 ms were simulated and stroke volume and systolic blood pressure showed clear peaks at 120 ms for 90 bpm. For a heart during exercise, the increase in cardiac output continues to 160 bpm. However, for a heart during pacing, those physiological parameter adjustments are removed that are related to changes in body oxygen requirements (preload, arterial stiffness and body resistance). Consequently, cardiac output increases initially with heart rate; as the heart rate goes up (>100 bpm), the increasing rate of cardiac output slows down and approaches a plateau.

Left Ventricle Biomechanics of Low-Flow, Low-Gradient Aortic Stenosis: A Patient-Specific Computational Model

This study aimed to create an imaging-derived patient-specific computational model of low-flow, low-gradient (LFLG) aortic stenosis (AS) to obtain biomechanics data about the left ventricle. LFLG AS is now a commonly recognized sub-type of aortic stenosis. There remains much controversy over its management, and investigation into ventricular biomechanics may elucidate pathophysiology and better identify patients for valve replacement. ECG-gated cardiac computed tomography images from a patient with LFLG AS were obtained to provide patient-specific geometry for the computational model. Surfaces of the left atrium, left ventricle (LV), and outflow track were segmented. A previously validated multi-scale, multi-physics computational human heart model was adapted to the patient-specific geometry, yielding a model consisting of 91,000 solid elements. This model was coupled to a virtual circulatory system and calibrated to clinically measured parameters from echocardiography and cardiac catheterization data. The simulation replicated key physiologic parameters within 10% of their clinically measured values. Global LV systolic myocardial stress was 7.1 ± 1.8 kPa. Mean stress of the basal, middle, and apical segments were 7.7 ± 1.8 kPa, 9.1 ± 3.8 kPa, and 6.4 ± 0.4 kPa, respectively. This is the first patient-specific computational model of LFLG AS based on clinical imaging. Low myocardial stress correlated with low ejection fraction and eccentric LV remodeling. Further studies are needed to understand how alterations in LV biomechanics correlates with clinical outcomes of AS.

Irx5 and transient outward K+ currents contribute to transmural contractile heterogeneities in the mouse ventricle

Previous studies have established that fast transmural gradients of transient outward K⁺ current (I_to,f) correlate with regional differences in action potential (AP) profile and excitation-contraction coupling (ECC) with high I_to,f expression in the epimyocardium (EPI) being associated with short APs and low contractility and vice versa. Herein, we investigated the effects of disrupted I_to,f gradient on contractile properties using mouse models of Irx5 knockout (Irx5-KO) for selective I_to,f elevation in the endomyocardium (ENDO) of the left ventricle (LV) and Kcnd2 ablation (K_V4.2-KO) for selective I_to,freduction in the EPI. Irx5-KO mice exhibited decreased global LV contractility in association with reductions in cell shortening and Ca²⁺ transient amplitudes in isolated ENDO but not EPI cardiomyocytes. Moreover, transcriptional profiling revealed that the primary effect of Irx5 ablation on ECC-related genes was to increase I_to,f gene expression (i.e. Kcnd2 and Kcnip2) in the ENDO, but not the EPI. Indeed, K_V4.2-KO mice showed selective increases in cell shortening and Ca²⁺ transients in isolated EPI cardiomyocytes, leading to enhanced ventricular contractility and mice lacking both Irx5 and Kcnd2 displayed elevated ventricular contractility comparable to K_V4.2-KO mice. Our findings demonstrate that the transmural electromechanical heterogeneities in the healthy ventricles depend on the Irx5-dependent I_to,f gradients. These observations provide a useful framework for assessing the molecular mechanisms underlying the alterations in contractile heterogeneity seen in the diseased heart.

Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods

Simulations of cardiac electrophysiology and mechanics have been reported to be sensitive to the microstructural anisotropy of the myocardium. Consequently, a personalized representation of cardiac microstructure is a crucial component of accurate, personalized cardiac biomechanical models. In-vivo cardiac Diffusion Tensor Imaging (cDTI) is a non-invasive magnetic resonance imaging technique capable of probing the heart's microstructure. Being a rather novel technique, issues such as low resolution, signal-to noise ratio, and spatial coverage are currently limiting factors. We outline four interpolation techniques with varying degrees of data fidelity, different amounts of smoothing strength, and varying representation error to bridge the gap between the sparse in-vivo data and the model, requiring a 3D representation of microstructure across the myocardium. We provide a workflow to incorporate in-vivo myofiber orientation into a left ventricular model and demonstrate that personalized modelling based on fiber orientations from in-vivo cDTI data is feasible. The interpolation error is correlated with a trend in personalized parameters and simulated physiological parameters, strains, and ventricular twist. This trend in simulation results is consistent across material parameter settings and therefore corresponds to a bias introduced by the interpolation method. This study suggests that using a tensor interpolation approach to personalize microstructure with in-vivo cDTI data, reduces the fiber uncertainty and thereby the bias in the simulation results.

Patient-Specific Analysis of Ascending Thoracic Aortic Aneurysm with the Living Heart Human Model

In ascending thoracic aortic aneurysms (ATAAs), aneurysm kinematics are driven by ventricular traction occurring every heartbeat, increasing the stress level of dilated aortic wall. Aortic elongation due to heart motion and aortic length are emerging as potential indicators of adverse events in ATAAs; however, simulation of ATAA that takes into account the cardiac mechanics is technically challenging. The objective of this study was to adapt the realistic Living Heart Human Model (LHHM) to the anatomy and physiology of a patient with ATAA to assess the role of cardiac motion on aortic wall stress distribution. Patient-specific segmentation and material parameter estimation were done using preoperative computed tomography angiography (CTA) and ex vivo biaxial testing of the harvested tissue collected during surgery. The lumped-parameter model of systemic circulation implemented in the LHHM was refined using clinical and echocardiographic data. The results showed that the longitudinal stress was highest in the major curvature of the aneurysm, with specific aortic quadrants having stress levels change from tensile to compressive in a transmural direction. This study revealed the key role of heart motion that stretches the aortic root and increases ATAA wall tension. The ATAA LHHM is a realistic cardiovascular platform where patient-specific information can be easily integrated to assess the aneurysm biomechanics and potentially support the clinical management of patients with ATAAs.

Cardiac mesh morphing method for finite element modeling of heart failure with preserved ejection fraction

Numerical modeling of heart biomechanics can realistically capture morphological variations in diseases and has been helpful in advancing our understanding of the physiology. Subject-specific models require anatomic representation of medical images, and it is desirable to have a consistently repeatable models for any given morphology. In this study, we propose a novel and easily adaptable cardiac reconstruction algorithm by morphing an existing discretized mesh of an advanced finite element (FE) model, to match anatomies acquired from porcine cardiac magnetic resonance imaging (cMRI) scans. The morphing algorithm involves iterative FE simulations with visco-hyperelastic material properties. The living heart porcine model (LHPM) was chosen as the input baseline FE mesh, in order to preserve detailed anatomical features that cannot be captured in routine scans such as myofiber orientations and conduction pathways. The algorithm was demonstrated for the recreation of porcine hearts of a healthy subject and of a subject induced with heart failure with preserved ejection fraction (HFpEF) conditions, where there were substantial hypertrophy and anatomical alterations. We further used the morphed meshes for FE modeling of cardiac contraction and relaxation, thus demonstrating the applicability of the proposed algorithm in producing viable meshes. The results show that our algorithm can recreate the characteristic anatomical changes of cardiac remodeling, including heart muscle thickening, as well as replicate the reduction in ventricular volume. This algorithm allows for the creation of subject-specific models with the same mesh connectivity, thus enabling spatial comparison and analysis of pathologic progress.

Computational Modeling Studies of the Roles of Left Ventricular Geometry, Afterload, and Muscle Contractility on Myocardial Strains in Heart Failure with Preserved Ejection Fraction

Global longitudinal strain and circumferential strain are found to be reduced in HFpEF, which some have interpreted that the global left ventricular (LV) contractility is impaired. This finding is, however, contradicted by a preserved ejection fraction (EF) and confounded by changes in LV geometry and afterload resistance that may also affect the global strains. To reconcile these issues, we used a validated computational framework consisting of a finite element LV model to isolate the effects of HFpEF features in affecting systolic function metrics. Simulations were performed to quantify the effects on myocardial strains due to changes in LV geometry, active tension developed by the tissue, and afterload. We found that only a reduction in myocardial contractility and an increase in afterload can simultaneously reproduce the blood pressures, EF and strains measured in HFpEF patients. This finding suggests that it is likely that the myocardial contractility is reduced in HFpEF patients. Graphical abstract.

Establishing the Biofidelity of a Multiphysics Finite Element Model of the Human Heart

PURPOSE

Accelerating development of new therapeutic cardiac devices remains a clinical and technical priority. High-performance computing and the emergence of functional and complex in silico models of human anatomy can be an engine to accelerate the commercialization of innovative, safe, and effective devices.

METHODS

An existing three-dimensional, nonlinear model of a human heart with flow boundary conditions was evaluated. Its muscular tissues were exercised using electrophysiological boundary conditions, creating a dynamic, electro-mechanical simulation of the kinetics of the human heart. Anatomic metrics were selected to characterize the functional biofidelity of the model based on their significance to the design of cardiac devices. The model output was queried through the cardiac cycle and compared to in vivo literature values.

RESULTS

For the kinematics of mitral and aortic valves and curvature of coronary vessels, the model's performance was at or above the 95th percentile range of the in vivo data from large patient cohorts. One exception was the kinematics of the tricuspid valve. The model's mechanical use environment would subject devices to generally conservative use conditions.

CONCLUSIONS

This conservative simulated use environment for heart-based medical devices, and its judicious application in the evaluation of medical devices is justified, but careful interpretation of the results is encouraged.

Effects of Left Ventricular Hypertrophy and Myocardial Stiffness on Myocardial Strain Under Preserved Ejection Fraction

Despite numerous experimental observations regarding heart failure with preserved ejection fraction (HFpEF), which is characterized mainly by left ventricular hypertrophy and a left ventricular ejection fraction over 50%, myocardial dynamics under HFpEF have not yet been fully clarified, particularly regarding the relationship between myocardial strain distribution and myocardial work. To address this issue, we numerically investigated radial distribution of myocardial strain during a cardiac cycle with fixed internal volume at the end of the systolic and diastolic phases under different mechanical conditions, such as those involving myocardial thickness and elasticity of myocardial fibers. The myocardium was a modeled as a visco-hyperelastic continuous material. This model was taken into account that active contractile stress along the myocardial fiber direction depends on membrane potential change. Our numerical results showed that both radial and circumferential strains decreased as wall thickness increased, which reflected cardiac hypertrophy, but that myocardial work became larger than that observed with thin ventricular walls. Further, the change in left ventricular diastolic internal pressure caused circumferential strain, while fiber stiffness contributed to radial strain. Since peak circumferential strain was well estimated by the maximum difference between total internal and myocardial volumes, measuring the epicardial contraction rate should be helpful in understanding patients with HFpEF.

Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

An understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

Increased myocardial stiffness more than impaired relaxation function limits cardiac performance during exercise in heart failure with preserved ejection fraction: a virtual patient study

Aims

The relative impact of left ventricular (LV) diastolic dysfunction (LVDD) and impaired left atrial (LA) function on cardiovascular haemodynamics in heart failure with preserved ejection fraction (HFpEF) is largely unknown. We performed virtual patient simulations to elucidate the relative effects of these factors on haemodynamics at rest and during exercise.

Methods and results

The CircAdapt cardiovascular system model was used to simulate cardiac haemodynamics in wide ranges of impaired LV relaxation function, increased LV passive stiffness, and impaired LA function. Simulations showed that LV ejection fraction (LVEF) was preserved (>50%), despite these changes in LV and LA function. Impairment of LV relaxation function decreased E/A ratio and mildly increased LV filling pressure at rest. Increased LV passive stiffness resulted in increased E/A ratio, LA dilation and markedly elevated LV filling pressure. Impairment of LA function increased E/A ratio and LV filling pressure, explaining inconsistent grading of LVDD using echocardiographic indices. Exercise simulations showed that increased LV passive stiffness exerts a stronger exercise-limiting effect than impaired LV relaxation function does, especially with impaired LA function.

Conclusion

The CircAdapt model enabled realistic simulation of virtual HFpEF patients, covering a wide spectrum of LVDD and related limitations of cardiac exercise performance, all with preserved resting LVEF. Simulations suggest that increased LV passive stiffness, more than impaired relaxation function, reduces exercise tolerance, especially when LA function is impaired. In future studies, the CircAdapt model can serve as a valuable platform for patient-specific simulations to identify the disease substrate(s) underlying the individual HFpEF patient's cardiovascular phenotype.

Impact of Aortic Stenosis on Myofiber Stress: Translational Application of Left Ventricle-Aortic Coupling Simulation

The severity of aortic stenosis (AS) has traditionally been graded by measuring hemodynamic parameters of transvalvular pressure gradient, ejection jet velocity, or estimating valve orifice area. Recent research has highlighted limitations of these criteria at effectively grading AS in presence of left ventricle (LV) dysfunction. We hypothesized that simulations coupling the aorta and LV could provide meaningful insight into myocardial biomechanical derangements that accompany AS. A realistic finite element model of the human heart with a coupled lumped-parameter circulatory system was used to simulate AS. Finite element analysis was performed with Abaqus FEA. An anisotropic hyperelastic model was assigned to LV passive properties, and a time-varying elastance function governed the LV active response. Global LV myofiber peak systolic stress (mean ± standard deviation) was 9.31 ± 10.33 kPa at baseline, 13.13 ± 10.29 kPa for moderate AS, and 16.18 ± 10.59 kPa for severe AS. Mean LV myofiber peak systolic strains were −22.40 ± 8.73%, −22.24 ± 8.91%, and −21.97 ± 9.18%, respectively. Stress was significantly elevated compared to baseline for moderate (p < 0.01) and severe AS (p < 0.001), and when compared to each other (p < 0.01). Ventricular regions that experienced the greatest systolic stress were (severe AS vs. baseline) basal inferior (39.87 vs. 30.02 kPa; p < 0.01), mid-anteroseptal (32.29 vs. 24.79 kPa; p < 0.001), and apex (27.99 vs. 23.52 kPa; p < 0.001). This data serves as a reference for future studies that will incorporate patient-specific ventricular geometries and material parameters, aiming to correlate LV biomechanics to AS severity.

Left Ventricular Contraction Duration Is the Most Powerful Predictor of Cardiac Events in LQTS: A Systematic Review and Meta-Analysis

Background: Long-QT syndrome (LQTS) is primarily an electrical disorder characterized by a prolonged myocardial action potential. The delay in cardiac repolarization leads to electromechanical (EM) abnormalities, which adds a diagnostic value for LQTS. Prolonged left ventricular (LV) contraction was identified as a potential risk for arrhythmia. The aim of this meta-analysis was to assess the best predictor of all EM parameters for cardiac events (CEs) in LQTS patients. Methods: We systematically searched all electronic databases up to March 2020, to select studies that assessed the relationship between echocardiographic indices—contraction duration (CD), mechanical dispersion (MD), QRS onset to peak systolic strain (QAoC), and the EM window (EMW); and electrical indices— corrected QT interval (QT_C), QT_C dispersion, RR interval in relation to CEs in LQTS. This meta-analysis included a total of 1041 patients and 373 controls recruited from 12 studies. Results: The meta-analysis showed that LQTS patients had electrical and mechanical abnormalities as compared to controls—QT_C, WMD 72.8; QT_C dispersion, WMD 31.7; RR interval, WMD 91.5; CD, WMD 49.2; MD, WMD 15.9; QAoC, WMD 27.8; and EMW, WMD −62.4. These mechanical abnormalities were more profound in symptomatic compared to asymptomatic patients in whom disturbances were already manifest, compared to controls. A CD ≥430 ms had a summary sensitivity (SS) of 71%, specificity of 84%, and diagnostic odds ratio (DOR) >19.5 in predicting CEs. EMW and QT_C had a lower accuracy. Conclusions: LQTS is associated with pronounced EM abnormalities, particularly prolonged LV myocardial CD, which is profound in symptomatic patients. These findings highlight the significant role of EM indices like CD in managing LQTS patients.

Prediction of Left Ventricular Mechanics Using Machine Learning

The goal of this paper was to provide a real-time left ventricular (LV) mechanics simulator using machine learning (ML). Finite element (FE) simulations were conducted for the LV with different material properties to obtain a training set. A hyperelastic fiber-reinforced material model was used to describe the passive behavior of the myocardium during diastole. The active behavior of the heart resulting from myofiber contractions was added to the passive tissue during systole. The active and passive properties govern the LV constitutive equation. These mechanical properties were altered using optimal Latin hypercube design of experiments to obtain training FE models with varied active properties (volume and pressure predictions) and varied passive properties (stress predictions). For prediction of LV pressures, we used eXtreme Gradient Boosting (XGboost) and Cubist, and XGBoost was used for predictions of LV pressures, volumes as well as LV stresses. The LV pressure and volume results obtained from ML were similar to FE computations. The ML results could capture the shape of LV pressure as well as LV pressure-volume loops. The results predicted by Cubist were smoother than those from XGBoost. The mean absolute errors were as follows: XGBoost volume: 1.734 ± 0.584 ml, XGBoost pressure: 1.544 ± 0.298 mmHg, Cubist volume: 1.495 ± 0.260 ml, Cubist pressure: 1.623 ± 0.191 mmHg, myofiber stress: 0.334 ± 0.228 kPa, cross myofiber stress: 0.075 ± 0.024 kPa, and shear stress: 0.050 ± 0.032 kPa. The simulation results show ML can predict LV mechanics much faster than the FE method. The ML model can be used as a tool to predict LV behavior. Training of our ML model based on a large group of subjects can improve its predictability for real world applications.

Method for Calibration of Left Ventricle Material Properties using 3D Echocardiography Endocardial Strains

We sought to calibrate mechanical properties of left ventricle (LV) based on 3D speckle tracking echocardiographic imaging data recorded from 16 segments defined by American Heart Association (AHA). The in vivo data were used to create finite element (FE) LV and biventricular (BV) models. The orientation of the fibers in the LV model was rule-based, but diffusion tensor magnetic resonance imaging (DT-MRI) data were used for the fiber directions in the BV model. A nonlinear fiber-reinforced constitutive equation was used to describe the passive behavior of the myocardium, whereas the active tension was described by a model based on tissue contraction (Tmax). Isight was used for optimization, which used Abaqus as the forward solver (Simulia, Providence, USA). The calibration of passive properties based on the end diastolic pressure volume relation (ED PVR) curve resulted in relatively good agreement (mean error = -0.04 ml). The difference between experimental and computational strains decreased after segmental strain metrics, rather than global metrics, were used for calibration: for the LV model, the mean difference reduced from 0.129 to 0.046 (circumferential) and from 0.076 to 0.059 (longitudinal); for the BV model, the mean difference nearly did not change in the circumferential direction (0.061) but reduced in the longitudinal direction from 0.076 to 0.055. The calibration of mechanical properties for myocardium can be improved using segmental strain metrics. The importance of realistic fiber orientation and geometry for modeling of the LV was shown.

Tricuspid Valve Regurgitation Decreases after MitraClip Implantation: Fluid Structure Interaction Simulation

Untreated tricuspid valve regurgitation (TR) is associated with increased rates of mortality, morbidity, and hospitalization. Current pharmacological and surgical treatment options for TR are limited. MitraClip (MC), an edge-to-edge percutaneous intervention, has been reported to be effective for treatment of TR. The goal of this study was to examine the effects of MC position on TR, using a multiphysics fluid-structure-interaction (FSI) analysis. The computational set up included the tricuspid valve (TV), the chordae tendineae, the blood particles, and a tube that surrounded the leaflets and blood particles. The leaflets and chordae were modeled as hyperelastic materials, and blood was modeled using smoothed particle hydrodynamics. FSI analysis was conducted for blood flow through the closed valve for multiple simulations that account for normal, diseased, and treated conditions of the TV. To simulate the diseased TV, a group of chordae between septal and pulmonary leaflets were removed from the normal TV, which produced increased regurgitation. Four MC treated scenarios were considered: i) one MC near the annulus, ii) one MC approximately midway between the annulus and leaflet tip, iii) one MC near the leaflet tip, iv) two MCs: one approximately midway between the annulus and leaflet tip, and one close to the leaflet tip. The TR increased in diseased TV (7.5%) compared to normal TV (2.5%). All MC treated scenarios decreased TR. The MC located near the midway point between the annulus and leaflet tip led to largest decrease in TR (75.2% compared to the untreated condition). The MC located near the leaflet tip was associated with lowest reduction in TR (2.2% compared to the untreated condition). When two MCs were used, reduction in TR was relatively high (68.7%), but TR was not improved compared to the optimal single MC. MC caused high stresses in the vicinity of the clipping area in all conditions; the highest occurred when the MC was near the leaflet tips. Using a quantitative computational approach, we confirm previous clinical reports on the efficacy of MC for treatment of TR. The results of this study could lead to the design of more efficient MC interventions for TR.

Intramyocardial Injections to De-Stiffen the Heart: A Subject-Specific in Silico Approach

We hypothesized that minimally invasive injections of a softening agent at strategic locations in stiff myocardium could de-stiffen the left ventricle (LV) globally. Physics-based finite element models of the LV were created from LV echocardiography images and pressures recorded during experiments in four swine. Results confirmed animal models of LV softening by systemic agents. Regional de-stiffening of myocardium led to global de-stiffening of LV. The mathematical set up was used to design LV global de-stiffening by regional softening of myocardium. At an end diastolic pressure of 23 mmHg, when 8 ml of the free wall was covered by intramyocardial injections, end diastolic volume (EDV) increased by 15.0%, whereas an increase up to 11 ml due to intramyocardial injections in the septum and free wall led to a 26.0% increase in EDV. Although the endocardial intramyocardial injections occupied a lower LV wall volume, they led to an EDV (44 ml) that was equal compared to intramyocardial injections in the mid-wall (44 ml) and larger compared to intramyocardial injections in the epicardium (41 ml). Using an in silico set up, sites of regional myocardium de-stiffening could be planned in order to globally soften overly stiff LV in heart failure with preserved ejection fraction. This novel treatment is built on subject-specific data. Hypothesis-testing of these simulation findings in animal models is warranted.