Passive Ventricular Mechanics Modelling Using MRI of Structure and Function

Abstract

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.

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

“…Using this integrated model, a finite deformation elasticity problem was solved for early diastolic filling to simulate the passive mechanics of the LV. In our previous study (Wang et al, 2008), we estimated mechanical stiffness by matching end-diastolic LV cavity volume. In this study, we have extended the estimation process to match the predicted localised motion of a large set of embedded material points with that derived from tagged MRI.…”

Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways

“…We depicted examples on a univentricular mesh in the left of Figure 4 . Cardiac mechanics models were first applied to measure passive stiffness in-vivo in healthy animals [ 128 – 130 ] and patients [ 131 – 134 ] using cine-MRI data, passive pressure-volume relationships or, alternatively, the Klotz curve. Mojsejenko et al [ 135 ] used diastolic cine-MRI and invasive LV pressure data collected from porcine hearts 1 week after infarction to estimate myocardium and scar stiffness, as well as changes in fibre orientation within the scar.…”

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

“…The availability of in vivo imaging has not only ensured that computational models can accurately represent the shape of intact hearts, but has also enabled in vivo mechanics analyses to be performed on a wide range of species (e.g. dog [ 35 ], sheep [ 36 ] and human [ 37 – 40 ]). Methods for constructing 3D anatomical models can be broadly categorized into three main approaches: (1) iterative nonlinear least-squares fitting [ 15 , 33 , 40 – 42 ], combining medical image registration with free-form deformation techniques to customize a generic mesh [ 43 ]; (2) volume mesh generation from binary masks or surface meshes of the myocardium performed by software packages such as Tarantula ( ), GHS3D ( ), CGAL ( ) and Simpleware ( ); and (3) cardiac atlases used for the construction of 3D models of the heart from non-invasive medical images [ 44 , 45 ].…”