A Novel Method for Quantifying Smooth Regional Variations in Myocardial Contractility Within an Infarcted Human Left Ventricle Based on Delay-Enhanced Magnetic Resonance Imaging

Abstract

Heart failure is increasing at an alarming rate, making it a worldwide epidemic. As the population ages and life expectancy increases, this trend is not likely to change. Myocardial infarction (MI)-induced adverse left ventricular (LV) remodeling is responsible for nearly 70% of heart failure cases. The adverse remodeling process involves an extension of the border zone (BZ) adjacent to an MI, which is normally perfused but shows myofiber contractile dysfunction. To improve patient-specific modeling of cardiac mechanics, we sought to create a finite element model of the human LV with BZ and MI morphologies integrated directly from delayed-enhancement magnetic resonance (DE-MR) images. Instead of separating the LV into discrete regions (e.g., the MI, BZ, and remote regions) with each having a homogeneous myocardial material property, we assumed a functional relation between the DE-MR image pixel intensity and myocardial stiffness and contractility--we considered a linear variation of material properties as a function of DE-MR image pixel intensity, which is known to improve the accuracy of the model's response. The finite element model was then calibrated using measurements obtained from the same patient--namely, 3D strain measurements-using complementary spatial modulation of magnetization magnetic resonance (CSPAMM-MR) images. This led to an average circumferential strain error of 8.9% across all American Heart Association (AHA) segments. We demonstrate the utility of our method for quantifying smooth regional variations in myocardial contractility using cardiac DE-MR and CSPAMM-MR images acquired from a 78-yr-old woman who experienced an MI approximately 1 yr prior. We found a remote myocardial diastolic stiffness of C(0) = 0.102 kPa, and a remote myocardial contractility of T(max) = 146.9 kPa, which are both in the range of previously published normal human values. Moreover, we found a normalized pixel intensity range of 30% for the BZ, which is consistent with the literature. Based on these regional myocardial material properties, we used our finite element model to compute patient-specific diastolic and systolic LV myofiber stress distributions, which cannot be measured directly. One of the main driving forces for adverse LV remodeling is assumed to be an abnormally high level of ventricular wall stress, and many existing and new treatments for heart failure fundamentally attempt to normalize LV wall stress. Thus, our noninvasive method for estimating smooth regional variations in myocardial contractility should be valuable for optimizing new surgical or medical strategies to limit the chronic evolution from infarction to heart failure.

Fig. 1

Relationship between local tissue viability (i.e., pixel intensity measured by DE-MR imaging, normalized by the maximal pixel intensity) and local passive stiffness, as well as local active contractility. For low pixel intensities, i.e., healthy myocardium, the local stiffness, and contractility are equal to their normal values. Conversely, for high pixel intensities, i.e., damaged myocardium, the local stiffness is much higher than normal, and the local contractility is null. We assumed linearly varying material properties across the BZ. The parameters ${\alpha }_1$ and ${\alpha }_2$ must be personalized for each patient.

Fig. 2

Magnetic resonance images used for model personalization. (a) 3D cine images are used for ventricular geometry, (b) 3D CSPAMM-MR images for tissue strain, and (c) 2D DE-MR images for tissue viability.

Fig. 3

(a) Finite-element mesh of the left ventricular geometry in early-diastole. The contours were created by manual segmentation of cine MR images in mevislab. The fully hexahedral mesh was generated with truegrid. (b) Generic fiber field prescribed to the mesh using custom vtkpython scripts. Helix angle varies transmurally from +60 deg at the endocardium to −60 deg at the epicardium. Transverse and sheet angles are null. (c) Endocardial surface at end-diastole (blue) and end-systole (red), extracted by manual segmentation of cine MR images in mevislab.

Fig. 4

Nonrigid registration of the viability data with the anatomical data, based on method in Ref. [24]. One short-axis slice of the anatomical mask is shown in blue (outside the ventricular wall) and red (inside), and the gray-scale viability map is superimposed. (a)–(c) Different iterations of the registration process showing initial mismatch and final match between the viability map and the anatomy.

Fig. 5

(a) Viability map in early diastole. Healthy regions (low pixel intensity) appear in blue, while the infarcted region (high pixel intensity) appears in red. (b) Personalized contractility map determined through numerical optimization. The colors are inverted compared to those in the viability map, so that the regions are consistent: healthy regions (high contractility) appear in blue, while the infarcted region (low contractility) appears in red. Note that because the core infarct area is rather small, the region with zero contractility (red) is small as well. (c) Contour plot, in a long-axis plane, of the 95% normalized pixel intensity, which corresponds, according to the material optimization, to the area with less than 10% contractility compared to the remote region. (d) Same contour plot, as in (c), in a midventricular short-axis plane.

Fig. 6

Comparison of the ventricular deformation at ED (left) and end-systole (right), predicted by the personalized finite-element model (red line) and measured by cine MRI, in short-axis (top) and long-axis (bottom) views. The overall deformation pattern is well reproduced by the model.

Fig. 7

(a) Myofiber stress at ED, in kPa. Because of increased stiffness, the infarcted region seems slightly less stressed than the remote region. (b) Myofiber stress at end-systole, in kPa. Because of reduced contractility, the total stress, which combines both passive and active stresses, is significantly lower in the BZ than in the region remote to the infarct.