Simplified post processing of cine DENSE cardiovascular magnetic resonance for quantification of cardiac mechanics

Abstract

Background: Cardiovascular magnetic resonance using displacement encoding with stimulated echoes (DENSE) is capable of assessing advanced measures of cardiac mechanics such as strain and torsion. A potential hurdle to widespread clinical adoption of DENSE is the time required to manually segment the myocardium during post-processing of the images. To overcome this hurdle, we proposed a radical approach in which only three contours per image slice are required for post-processing (instead of the typical 30-40 contours per image slice). We hypothesized that peak left ventricular circumferential, longitudinal and radial strains and torsion could be accurately quantified using this simplified analysis.

Methods and results: We tested our hypothesis on a large multi-institutional dataset consisting of 541 DENSE image slices from 135 mice and 234 DENSE image slices from 62 humans. We compared measures of cardiac mechanics derived from the simplified post-processing to those derived from original post-processing utilizing the full set of 30-40 manually-defined contours per image slice. Accuracy was assessed with Bland-Altman limits of agreement and summarized with a modified coefficient of variation. The simplified technique showed high accuracy with all coefficients of variation less than 10% in humans and 6% in mice. The accuracy of the simplified technique was also superior to two previously published semi-automated analysis techniques for DENSE post-processing.

Conclusions: Accurate measures of cardiac mechanics can be derived from DENSE cardiac magnetic resonance in both humans and mice using a simplified technique to reduce post-processing time by approximately 94%. These findings demonstrate that quantifying cardiac mechanics from DENSE data is simple enough to be integrated into the clinical workflow.

Figure 1

Cine DENSE analysis. Tissue displacements are encoded into the phase of the signal (A) and using endocardial and epicardial boundaries (green and red, respectively), it is possible to derive a displacement field (B). This displacement field is then used to deform a mesh of the resting configuration (C) from which we can derive regional cardiac mechanics such as strain (D).

Figure 2

Simplified contour generation. Simplified contours (Solid Lines) were generated from the original user-defined contours (Dashed Lines) for end-diastolic and end-systolic images. The epicardial contour (red) was copied from the end-diastolic frame to all other images. The end-systolic endocardial boundary (yellow) was copied to all other images with the exception of the resting configuration (green). Frame numbers are given in the bottom right of each image.

Figure 3

Representative circumferential strain curves. Representative circumferential strain curves for a mid-ventricular short-axis image are shown for the original analysis method, our simplified analysis method, and a separate observer’s analysis using the original method.

Figure 4

Bland Altman limits of agreement for peak circumferential strain. Simplified analysis resulted in very tight agreement of peak circumferential strain (Ecc) values compared to the original contours in mice (top left) and humans (bottom left). This agreement is markedly better than the corresponding inter-observer variability in peak circumferential strain values using the original analysis (right). Note that all strain values are in units of absolute strain i.e. they are not normalized.

Figure 5

Coefficient of variation between peak strain values. There was superior agreement between the simplified and original analysis (black bars) compared to the inter-observer agreement from the original analysis (gray bars). The inter-observer agreement of the simplified analysis (white bars) was comparable to that of the original analysis in mice (left) and humans (right).

Figure 6

Root mean squared error between strain values. There was superior agreement between the simplified and original analysis (black bars) compared to the inter-observer agreement from the original analysis (white bars) in both mice (left) and humans (right). (*indicates p <0.001).

Figure 7

Comparison with semi- automated techniques. The agreement between the simplified and original analysis (black bars) was superior to both inter-observer error from the original analysis (white bars) and other automated methods (gray bars) in both mice (left) and humans (right). (*Gilliam data was obtained from the original manuscript [17] where data was only provided for human subjects).