The estimation of patient-specific cardiac diastolic functions from clinical measurements.

An unresolved issue in patients with diastolic dysfunction is that the estimation of myocardial stiffness cannot be decoupled from diastolic residual active tension (AT) because of the impaired ventricular relaxation during diastole. To address this problem, this paper presents a method for estimating diastolic mechanical parameters of the left ventricle (LV) from cine and tagged MRI measurements and LV cavity pressure recordings, separating the passive myocardial constitutive properties and diastolic residual AT. Dynamic C1-continuous meshes are automatically built from the anatomy and deformation captured from dynamic MRI sequences. Diastolic deformation is simulated using a mechanical model that combines passive and active material properties. The problem of non-uniqueness of constitutive parameter estimation using the well known Guccione law is characterized by reformulation of this law. Using this reformulated form, and by constraining the constitutive parameters to be constant across time points during diastole, we separate the effects of passive constitutive properties and the residual AT during diastolic relaxation. Finally, the method is applied to two clinical cases and one control, demonstrating that increased residual AT during diastole provides a potential novel index for delineating healthy and pathological cases

Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction

In this research, we hypothesized that novel biomechanical parameters are discriminative in patients following acute ST-segment elevation myocardial infarction (STEMI). To identify these biomechanical biomarkers and bring computational biomechanics ‘closer to the clinic’, we applied state-of-the-art multiphysics cardiac modelling combined with advanced machine learning and multivariate statistical inference to a clinical database of myocardial infarction. We obtained data from 11 STEMI patients (ClinicalTrials.gov NCT01717573) and 27 healthy volunteers, and developed personalized mathematical models for the left ventricle (LV) using an immersed boundary method. Subject-specific constitutive parameters were achieved by matching to clinical measurements. We have shown, for the first time, that compared with healthy controls, patients with STEMI exhibited increased LV wall active tension when normalized by systolic blood pressure, which suggests an increased demand on the contractile reserve of remote functional myocardium. The statistical analysis reveals that the required patient-specific contractility, normalized active tension and the systolic myofilament kinematics have the strongest explanatory power for identifying the myocardial function changes post-MI. We further observed a strong correlation between two biomarkers and the changes in LV ejection fraction at six months from baseline (the required contractility (r = − 0.79, p < 0.01) and the systolic myofilament kinematics (r = 0.70, p = 0.02)). The clinical and prognostic significance of these biomechanical parameters merits further scrutinization

Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

International audienceComputer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive power. However the personalisation step to go from a generic model to a patient-specific one is still a scientific challenge. In particular it is still difficult to quantify the uncertainty on the estimated parameters and predicted values. In this manuscript we present a new pipeline to evaluate the impact of fibre uncertainty on the personalisation of an electromechanical model of the heart from ECG and medical images. We detail how we estimated the variability of the fibre architecture among a given population and how the uncertainty generated by this variability impacts the following personalisation. We first show the variability of the personalised simulations, with respect to the principal variations of the fibres. Then discussed how the variations in this (small) healthy population of fibres impact the parameters of the personalised simulations

ESTIMATING PASSIVE MATERIAL PROPERTIES AND FIBER ORIENTATION IN A MYOCARDIAL INFARCTION THROUGH AN OPTIMIZATION SCHEME USING MRI AND FE SIMULATION

Myocardial infarctions induce a maladaptive ventricular remodeling process that independently contributes to heart failure. In order to develop effective treatments, it is necessary to understand the way and extent to which the heart undergoes remodeling over the course of healing. There have been few studies to produce any data on the in-vivo material properties of infarcts, and much less on the properties over the time course of healing. In this paper, the in-vivo passive material properties of an infarcted porcine model were estimated through a combined use of magnetic resonance imaging, catheterization, finite element modeling, and a genetic algorithm optimization scheme. The collagen fiber orientation at the epicardial and endocardial surfaces of the infarct were included in the optimization. Data from porcine hearts (N=6) were taken at various time points after infarction, specifically 1 week, 4 weeks, and 8 weeks post-MI. The optimized results shared similarities with previous studies. In particular, the infarcted region was shown to dramatically increase in stiffness at 1 week post-MI. There was also evidence of a subsequent softening of the infarcted region at later time points post infarction. Fiber orientation results varied greatly but showed a shift toward a more circumferential orientation

COMPUTATIONAL INVESTIGATION OF TRANSMURAL DIFFERENCES IN LEFT VENTRICULAR CONTRACTILITY AND HYDROGEL INJECTION TREATMENT FOR MYOCARDIAL INFARCTION

Heart failure (HF) is one of the leading causes of death and impacts millions of people throughout the world. Recently, injectable hydrogels have been developed as a potential new therapy to treat myocardium infarction (MI). This dissertation is focused on two main topics: 1) to gain a better understanding the transmural contractility in the healthy left ventricle (LV) wall and 2) investigate the efficacy of the hydrogel injection treatment on LV wall stress and function. The results indicate that a non-uniform distribution of myocardial contractility in the LV wall provide a better representation of normal LV function. The other important study explored the influence altering the stiffness of the biomaterial hydrogel injections. These results show that a larger volume and higher stiffness injection reduce myofiber stress the most and maintaining the wall thickness during loading. The computational approach developed in this dissertation could be used in the future to evaluate the optimal properties of the hydrogel. The last study used a combination of MRI, catheterization, finite element (FE) modeling to investigate the effects of hydrogel injection on borderzone (BZ) contractility after MI. The results indicate that the treatment with hydrogel injection significantly improved BZ function and reduce LV remodeling, via altered MI properties. Additionally, the wall thickness in the infarct and BZ regions were significantly higher in the treated case. Conclusion: hydrogel injection could be a valuable clinical therapy for treating MI

Computational Modeling of Cardiac Biomechanics

The goal of this dissertation was to develop a realistic and patient-specific computational model of the heart that ultimately would help medical scientists to better diagnose and treat heart diseases. In order to achieve this goal, a three dimensional finite element model of the heart was created using magnetic resonance images of the beating pig heart. This model was loaded by the pressure of blood inside the left ventricle which was measured by synchronous catheterization. A recently developed structurally based constitutive model of the myocardium was incorporated in the finite element solver to model passive left ventricular myocardium. Additionally, an unloading algorithm originally designed for arteries was adapted to estimate the stress-free geometry of the heart from its partially-loaded geometry obtained from magnetic resonance imaging. Finally, a regionally varying growth module was added to the computational model to predict eccentric hypertrophy of the heart under various pathological conditions that result in volume overload of the heart. The computational model was validated using experimental data obtained from porcine heart such as in vivo strains measured from magnetic resonance imaging