Vicky Y. Wang

Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function

The majority of patients with clinically diagnosed heart failure have normal systolic pump function and are commonly categorized as suffering from diastolic heart failure. The left ventricle (LV) remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions, which in turn can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element (FE) model was customized to geometric data segmented from in vivo tagged magnetic resonance images (MRI) data and myofibre orientation derived from ex vivo diffusion tensor MRI (DTMRI) of a canine heart using nonlinear finite element fitting techniques. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion in each voxel of a DTMRI directly corresponds to the local myocardial fibre orientation. Due to differences in myocardial geometry between in vivo and ex vivo imaging, myofibre orientations were mapped into the geometric FE model using host mesh fitting (a free form deformation technique). Pressure recordings, temporally synchronized to the tagging data, were used as the loading constraints to simulate the LV deformation during diastole. Simulation of diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. Integrated physiological modelling of this kind will allow more insight into mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction under pathological conditions.

Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics.

With heart and cardiovascular diseases continually challenging healthcare systems worldwide, translating basic research on cardiac (patho)physiology into clinical care is essential. Exacerbating this already extensive challenge is the complexity of the heart, relying on its hierarchical structure and function to maintain cardiovascular flow. Computational modelling has been proposed and actively pursued as a tool for accelerating research and translation. Allowing exploration of the relationships between physics, multiscale mechanisms and function, computational modelling provides a platform for improving our understanding of the heart. Further integration of experimental and clinical data through data assimilation and parameter estimation techniques is bringing computational models closer to use in routine clinical practice. This article reviews developments in computational cardiac modelling and how their integration with medical imaging data is providing new pathways for translational cardiac modelling.

Myocardial contractility and regional work throughout the cardiac cycle using FEM and MRI

The role of myocardial contractile force in the progression of cardiovascular diseases such as heart failure (HF) has been the focus of many studies. In order to better understand the mechanisms underlying compromised contractility, finite element (FE) modelling of ventricular mechanics is a useful tool. Distributions of active fibre stress during systole were estimated using left ventricular (LV) FE models that incorporated in vivo MRI tagging data and concurrent LV endocardial pressure recordings to parameterise a time-varying model of myocardial contraction. For five canine hearts, the calcium dependent contractile stress increased to peaks ranging from 33 kPa to 57 kPa during systole. Regional distributions of fibre stretch, stress, and myocardial work were examined in each case. Using this type of integrative biophysical modelling to compare normal and pathological cases will elucidate the underlying physiological mechanisms of cardiac mechanical dysfunction.

Cardiac active contraction parameters estimated from magnetic resonance imaging

Impaired systolic ventricular function is common in patients diagnosed with heart failure (HF) or ischaemic heart disease. The diminished contractile performance with impaired contractility (systolic HF) can be induced by impaired filling function (diastolic HF) and the wall stress (both passive and active) may indicate the progression from diastolic HF to systolic HF. In order to better understand the distribution of active stress during ventricular contraction, a left ventricular (LV) finite element (FE) model incorporating LV fibre geometry and function was developed to parameterise a time-varying model of myocardial contraction by simulating LV mechanics. During systole, the isometric active stress monotonically increased to 95 kPa, and rapidly recovered during isovolumic relaxation. We also observed regional variations of the fibre length dependent contractile stress throughout the LV. The time-varying active stress curve thereby obtained enabled quantification of heart muscle performance. This type of integrative modelling enables the investigation of LV mechanics on an individualised basis.

Changes in in vivo myocardial tissue properties due to heart failure

A clinical image data driven mechanics analysis was used to quantify changes in tissue-specific passive and contractile material properties for groups of normal and HF patients. We have developed an automated mechanics modelling framework to firstly construct left ventricular (LV) mechanics models based on shape information derived from non-invasive dynamic magnetic resonance images, then to characterise passive tissue stiffness and maximum contractile stress by matching the simulated LV mechanics with data from the dynamic cardiac images. Preliminary statistical analysis revealed that patients with hypertrophy or non-ischemic heart failure exhibited increased passive myocardial stiffness compared to the normals. Elevated maximum contractile stress was also observed for hypertrophic patients. Tissue-specific parameter estimation analysis of this kind can potentially be applied in the clinical setting to provide a more specific disease measure to assist with stratification of HF patients.

Automated personalised human left ventricular FE models to investigate heart failure mechanics

We have developed finite element modelling techniques to semi-automatically generate personalised biomechanical models of the human left ventricle (LV) based on cardiac magnetic resonance images. Geometric information of the LV throughout the cardiac cycle was derived via semi-automatic segmentation using non-rigid image registration with a pre-segmented image. A reference finite element mechanics model was automatically fitted to the segmented LV endocardial and epicardial surface data at diastasis. Passive and contractile myocardial mechanical properties were then tuned to best match the segmented surface data at end-diastole and end-systole, respectively. Global and regional indices of myocardial mechanics, including muscle fibre stress and extension ratio were then quantified and analysed. This mechanics modelling framework was applied to a healthy human subject and a patient with non-ischaemic heart failure. Comparison of the estimated passive stiffness and maximum activation level between the normal and diseased cases provided some preliminary insight into the changes in myocardial mechanical properties during heart failure. This automated approach enables minimally invasive personalised characterisation of cardiac mechanical function in health and disease. It also has the potential to elucidate the mechanisms of heart failure, and provide new quantitative diagnostic markers and therapeutic strategies for heart failure.

Passive Ventricular Mechanics Modelling Using MRI of Structure and Function

Patients suffering from dilated cardiomyopathy or myocardial infarction can develop left ventricular (LV) diastolic impairment. The LV remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions and this remodeling process can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element model was developed to incorporate physiological and mechanical information derived from in vivo magnetic resonance imaging (MRI) tissue tagging, in vivo LV cavity pressure recording and ex vivo diffusion tensor MRI (DTMRI) of a canine heart. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion (the primary eigenvector) in each voxel of a DTMRI directly correlates with the myocardial fibre orientation. This model was customized to the geometry of the canine LV during diastasis by fitting the segmented epicardial and endocardial surface data from tagged MRI using nonlinear finite element fitting techniques. Myofibre orientations, extracted from DTMRI of the same heart, were incorporated into this geometric model using a free form deformation methodology. Pressure recordings, temporally synchronized to the tissue tagging MRI data, were used to simulate the LV deformation during diastole. Simulation of the diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. This integrated physiological model will allow more insight into the regional passive diastolic mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction in pathological conditions.

Image-Based Investigation of Human in Vivo Myofibre Strain

Cardiac myofibre deformation is an important determinant of the mechanical function of the heart. Quantification of myofibre strain relies on 3D measurements of ventricular wall motion interpreted with respect to the tissue microstructure. In this study, we estimated in vivo myofibre strain using 3D structural and functional atlases of the human heart. A finite element modelling framework was developed to incorporate myofibre orientations of the left ventricle (LV) extracted from 7 explanted normal human hearts imaged ex vivo with diffusion tensor magnetic resonance imaging (DTMRI) and kinematic measurements from 7 normal volunteers imaged in vivo with tagged MRI. Myofibre strain was extracted from the DTMRI and 3D strain from the tagged MRI. We investigated: i) the spatio-temporal variation of myofibre strain throughout the cardiac cycle; ii) the sensitivity of myofibre strain estimates to the variation in myofibre angle between individuals; and iii) the sensitivity of myofibre strain estimates to variations in wall motion between individuals. Our analysis results indicate that end systolic (ES) myofibre strain is approximately homogeneous throughout the entire LV, irrespective of the inter-individual variation in myofibre orientation. Additionally, inter-subject variability in myofibre orientations has greater effect on the variabilities in myofibre strain estimates than the ventricular wall motions. This study provided the first quantitative evidence of homogeneity of ES myofibre strain using minimally-invasive medical images of the human heart and demonstrated that image-based modelling framework can provide detailed insight to the mechanical behaviour of the myofibres, which may be used as a biomarker for cardiac diseases that affect cardiac mechanics.

Estimation of in vivo human myocardial fibre strain by integrating diffusion tensor and tagged MRI using FE modelling

In this study, we propose a methodology to estimate 3D+time maps of left ventricular fibre strain from human structural and dynamic MRI data. A finite element model integrates fibre principal direction throughout the left ventricle from an ex vivo human diffusion tensor MRI acquisition and motion from tagged MRI. This combination enables the estimation of fibre strain and its variation throughout the cardiac cycle. The long-term goal of this study is to apply this technique to an atlas of human fibre orientations and to investigate the importance of having subject-specific fibre orientation for fibre strain analysis.

Image-driven constitutive modeling of myocardial fibrosis

ABSTRACT Myocardial fibrosis is a pathological process that occurs during heart failure (HF). It involves microstructural remodeling of normal myocardial tissue, and consequent changes in both cardiac geometry and function. The role of myocardial structural remodeling in the progression of HF remains poorly understood. We propose a constitutive modeling framework, informed by high-resolution images of cardiac tissue structure, to model the mechanical response of normal and fibrotic myocardium. This image-driven constitutive modeling approach allows us to better reproduce and understand the relationship between structural and functional remodeling of ventricular myocardium during HF.