Characterization of diffuse fibrosis in the failing human heart via diffusion tensor imaging and quantitative histological validation

Abstract

Non-invasive imaging techniques are highly desirable as an alternative to conventional biopsy for the characterization of the remodeling of tissues associated with disease progression, including end-stage heart failure. Cardiac diffusion tensor imaging (DTI) has become an established method for the characterization of myocardial microstructure. However, the relationships between diffuse myocardial fibrosis, which is a key biomarker for staging and treatment planning of the failing heart, and measured DTI parameters have yet to be investigated systematically. In this study, DTI was performed on left ventricular specimens collected from patients with chronic end-stage heart failure as a result of idiopathic dilated cardiomyopathy (n = 14) and from normal donors (n = 5). Scalar DTI parameters, including fractional anisotropy (FA) and mean (MD), primary (D1 ), secondary (D2 ) and tertiary (D3 ) diffusivities, were correlated with collagen content measured by digital microscopy. Compared with hearts from normal subjects, the FA in failing hearts decreased by 22%, whereas the MD, D2 and D3 increased by 12%, 14% and 24%, respectively (P < 0.01). No significant change was detected for D1 between the two groups. Furthermore, significant correlation was observed between the DTI scalar indices and quantitative histological measurements of collagen (i.e. fibrosis). Pearson's correlation coefficients (r) between collagen content and FA, MD, D2 and D3 were -0.51, 0.59, 0.56 and 0.62 (P < 0.05), respectively. The correlation between D1 and collagen content was not significant (r = 0.46, P = 0.05). Computational modeling analysis indicated that the behaviors of the DTI parameters as a function of the degree of fibrosis were well explained by compartmental exchange between myocardial and collagenous tissues. Combined, these findings suggest that scalar DTI parameters can be used as metrics for the non-invasive assessment of diffuse fibrosis in failing hearts.

Keywords: DTI; diffuse fibrosis; fractional anisotropy; histological correlation; idiopathic dilated cardiomyopathy; mean diffusivity; non-ischemic heart failure; principal diffusivities.

Figure 1

Representative DTI scalar maps from control and failing heart cores. The top panel shows a MR image without diffusion weighting (B0), FA, MD, D₁, D₂, and D₃ maps for normal (a-f), whereas the lower panel shows the failing core (g-l). A transverse view is shown, with the endocardium located at the top. The pericardial fat visible in the B0 image (white arrows in a and g) was segmented out in the DTI processing step. The DTI parameters are shown in falsecolor according to the colorbars, and all diffusivities had the same color scaling with units of 10^-3 mm²/s.

Figure 2

Histological evaluation of control and failing heart cores. Masson's trichrome histological images of collagen content (20x magnification, collagen stains in blue) from a representative control (a) and failing (b) heart cores. The black arrow in (a) points to perivascular collagen, which was included as part of the total collagen calculation reported in this study.

Figure 3

Quantitative correlation between DTI scalar parameters and collagen content. Scatter plots of FA, D₁, D₂ and D₃ as a function of collagen content are shown with the experimental data (displayed as solid squares for the control group, and as x for the failing group) and the linear regression fit (solid black line) and the 95% prediction interval (dashed lines). The linear regression coefficient (r) and its corresponding P value are reported with each plot.

Figure 4

Monte Carlo computer simulation of DTI scalar parameters as function of collagen content (%). The standard DTI measurements were simulated when the underlying diffusion signal was assumed to originate from either fast (dotted blue line, with upper standard deviation error bars) or slow (solid red line with lower standard deviation error bars) exchange between the two compartments. The experimental data and their regression fit line are overlaid on each plot in gray (x for individual measurements, and solid gray line for the linear regression fit). Little difference was seen between fast and slow exchange model predictions compared to the variability observed in the experimental data.