Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

From medical images to individualized cardiac mechanics: A Physiome approach

Cardiac mechanics is a branch of science that deals with forces, kinematics, and material properties of the heart, which is valuable for clinical applications and physiological studies. Although anatomical and biomechanical experiments are necessary to provide the fundamental knowledge of cardiac mechanics, the invasive nature of the procedures limits their further applicability. In consequence, noninvasive alternatives are required, and cardiac images provide an excellent source of subject-specific and in vivo information. Noninvasive and individualized cardiac mechanical studies can be achieved through coupling general physiological models derived from invasive experiments with subject-specific information extracted from medical images. Nevertheless, as data extracted from images are gross, sparse, or noisy, and do not directly provide the information of interest in general, the couplings between models and measurements are complicated inverse problems with numerous issues need to be carefully considered. The goal of this research is to develop a noninvasive framework for studying individualized cardiac mechanics through systematic coupling between cardiac physiological models and medical images according to their respective merits. More specifically, nonlinear state-space filtering frameworks for recovering individualized cardiac deformation and local material parameters of realistic nonlinear constitutive laws have been proposed. To ensure the physiological meaningfulness, clinical relevance, and computational feasibility of the frameworks, five key issues have to be properly addressed, including the cardiac physiological model, the heart representation in the computational environment, the information extraction from cardiac images, the coupling between models and image information, and also the computational complexity. For the cardiac physiological model, a cardiac physiome model tailored for cardiac image analysis has been proposed to provide a macroscopic physiological foundation for the study. For the heart representation, a meshfree method has been adopted to facilitate implementations and spatial accuracy refinements. For the information extraction from cardiac images, a registration method based on free-form deformation has been adopted for robust motion tracking. For the coupling between models and images, state-space filtering has been applied to systematically couple the models with the measurements. For the computational complexity, a mode superposition approach has been adopted to project the system into an equivalent mathematical space with much fewer dimensions for computationally feasible filtering. Experiments were performed on both synthetic and clinical data to verify the proposed frameworks

A finite strain nonlinear human mitral valve model with fluid structure interaction

A simulated human mitral valve under a physiological pressure loading is developed using a hybrid finite element immersed boundary method, which incorporates experimentally based constitutive laws in a three-dimensional fluid-structure interaction framework. A transversely isotropic material constitutive model is used for characterizing the mechanical behaviour of the mitral valve tissue based on recent mechanical tests of healthy human mitral leaflets. Our results show good agreement, in terms of the flow rate and the closing and opening configurations, with the measurements from the magnetic resonance images. The stresses in the anterior leaflet are found to be higher than those in the posterior leaflet, and concentrated around the annulus trigons and free edges of the valve leaflets. Those areas are located where the leaflet has the highest curvature. Effects of the chordae tendineae in the material model are studied and the results show that these chordae play an important role in providing a secondary orifice for the flow when valve opens. Although there are some discrepancies to be overcome in future works, our simulations show that the developed computational model is promising in mimicking the in vivo mitral valve dynamics and providing important information that are not obtainable by in vivo measurements. This article is protected by copyright. All rights reserved

Modelling mitral valvular dynamics–current trend and future directions

Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed

Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models

Biomechanical computational models have potential prognostic utility in patients after an acute ST-segment–elevation myocardial infarction (STEMI). In a proof-of-concept study, we defined two groups (1) an acute STEMI group (n = 6, 83% male, age 54 ± 12 years) complicated by left ventricular (LV) systolic dysfunction; (2) an age- and sex- matched hyper-control group (n = 6, 83% male, age 46 ± 14 years), no prior history of cardiovascular disease and normal systolic blood pressure (SBP < 130 mmHg). Cardiac MRI was performed in the patients (2 days & 6 months post-STEMI) and the volunteers, and biomechanical heart models were synthesized for each subject. The candidate parameters included normalized active tension (ATnorm) and active tension at the resting sarcomere length (Treq, reflecting required contractility). Myocardial contractility was inversely determined from personalized heart models by matching CMR-imaged LV dynamics. Compared with controls, patients with recent STEMI exhibited increased LV wall active tension when normalized by SBP. We observed a linear relationship between Treq 2 days post-MI and global longitudinal strain 6 months later (r = 0.86; p = 0.03). Treq may be associated with changes in LV function in the longer term in STEMI patients complicated by LV dysfunction. Further studies seem warranted

Analysis of myocardial motion using generalized spline models and tagged magnetic resonance images

Heart wall motion abnormalities are the very sensitive indicators of common heart diseases, such as myocardial infarction and ischemia. Regional strain analysis is especially important in diagnosing local abnormalities and mechanical changes in the myocardium. In this work, we present a complete method for the analysis of cardiac motion and the evaluation of regional strain in the left ventricular wall. The method is based on the generalized spline models and tagged magnetic resonance images (MRI) of the left ventricle. The whole method combines dynamical tracking of tag deformation, simulating cardiac movement and accurately computing the regional strain distribution. More specifically, the analysis of cardiac motion is performed in three stages. Firstly, material points within the myocardium are tracked over time using a semi-automated snake-based tag tracking algorithm developed for this purpose. This procedure is repeated in three orthogonal axes so as to generate a set of one-dimensional sample measurements of the displacement field. The 3D-displacement field is then reconstructed from this sample set by using a generalized vector spline model. The spline reconstruction of the displacement field is explicitly expressed as a linear combination of a spline kernel function associated with each sample point and a polynomial term. Finally, the strain tensor (linear or nonlinear) with three direct components and three shear components is calculated by applying a differential operator directly to the displacement function. The proposed method is computationally effective and easy to perform on tagged MR images. The preliminary study has shown potential advantages of using this method for the analysis of myocardial motion and the quantification of regional strain

A Novel Composite Material-based Computational Model for Left Ventricle Biomechanics Simulation

To model cardiac mechanics effectively, various mechanical characteristics of cardiac muscle tissue including anisotropy, hyperelasticity, and tissue active contraction characteristics must be considered. Some of these features cannot be implemented using commercial finite element (FE) solvers unless additional custom-developed computer codes/subroutines are appended. Such codes/subroutines are unavailable for the research community. Accordingly, the overarching objective of this research is to develop a novel LV mechanics model which is implementable in commercial FE solvers and can be used effectively within inverse FE frameworks towards cardiac disease diagnosis and therapy. This was broken down into a number of objectives. The first objective is to develop a novel cardiac tissue mechanical model. This model was constructed of microstructural cardiac tissue constituents while their associated volume contributions and mechanical properties were incorporated into the model. These constituents were organized in small FE tissue specimen models consistent with the normal/pathological cardiac tissue microstructure. In silico biaxial/uniaxial mechanical tests were conducted on the specimen models and corresponding stress-strain data were validated by comparing them with cardiac tissue data reported in the literature. Another objective of this research is developing a novel FE-based mechanical model of the LV which is fully implementable using commercial FE solvers without requiring further coding, potentially leading to a computationally efficient model which is easily adaptable to diverse pathological conditions. This was achieved through considering a novel composite material model of the cardiac tissue while all aspects of the cardiac mechanics including hyperelasticity, anisotropy, and active tissue responses were preserved. The model was applied to an in silico geometry of a canine LV under both normal and pathological conditions and systolic/diastolic responses of the model were compared with corresponding data of other LV mechanical models and LV contraction measurements. To test the suitability of the proposed cardiac model for FE inversion-based algorithms, the model was utilized for LV diastolic mechanical simulation to estimate the tissue stiffness and blood pressure using an ad-hoc optimization scheme. This led to reasonable tissue stiffness and blood pressure values falling within the range of LV measurements of healthy subjects, confirming the efficacy of this model for inversion-based diagnosis applications

Shape Analysis Based Strategies for Evaluation of Adaptations in In Vivo Right Ventricular Geometry and Mechanics as Effected by Pulmonary Hypertension

Pulmonary hypertension (PH) is a deadly disease, which as it progresses over time alters many aspects of the afflicted heart, and particularly the right ventricle (RV), such as its size, shape, and mechanical material properties. However, due to the limitations of what can be measured noninvasively in a standard clinical setting and the difficulty caused by the intrinsic complexity of the human RV, there has been little success to-date to identify clinically obtainable metrics of RV shape, deformation, or material properties that are quantitatively linked to the onset and progression of PH. Towards addressing this challenge, this work proposes the use of the shape and shape change of the RV, which is measurable from standard clinical imaging, along with statistical analysis and inverse material characterization strategies to identify new metrics of RV mechanical function that will be uniquely predictive of the state of the heart subject to PH. Thus, this thesis can be broken into two components: the first is statistical shape analysis of the RV, and the second is inverse characterization of heart wall mechanical material properties from RV shape change and measurable hemodynamics. For the statistical shape analysis investigation, a custom approach using harmonic mapping and proper orthogonal decomposition is applied to determine the fundamental components of shape (i.e., modes) from a dataset of 50 patients with varying states of PH, including some without PH at all. For the inverse characterization work, a novel method was developed to estimate the heterogeneous properties of a structure, given only the target shape of that structure, after a known excitation is applied to deform the structure. Lastly, the inverse characterization algorithm was extended to be applicable to actual in vivo cardiac data, particularly through the inclusion of a registration step to account for the organ-scale rotation and translation of the heart. Future work remains to expand on the computational efficiency of this inverse solution estimation procedure, and to further evaluate and improve upon the consistency and clinical interpretability of the material property estimates

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