Endocardial center motion for quantification of left ventricular discoordination in heart failure using cine MRI.

OBJECTIVE: To compare a novel cardiovascular magnetic resonance technique for the assessment of left ventricular (LV) mechanical discoordination by characterizing the endocardial center motion (ECM) in short-axis cine MRI in healthy volunteers and heart failure patients with left bundle branch block (HF-LBBB). APPROACH: To evaluate ECM analysis as mechanical discoordination measure, we retrospectively compared spatial and temporal features of the ECM between a group of healthy volunteers (n = 14) and conduction defect patients (HF-LBBB, n = 31). We tracked the center of the endocardial borders on short-axis view MRI cine loops during the cardiac cycle. From the ECM trajectory we calculated the overall traveled distance, the enclosed area, the eccentricity of the trajectory, and the maximum traveled distance. The ECM can be visualized in spatial coordinates as well as by its temporal behavior. We evaluated the classification performance of these measures for LBBB detection. We also quantified the coherence of the ECM on the longitudinal direction by considering the variability of the ECM measures between different short-axis slices. MAIN RESULTS: Patients with LBBB showed significantly higher traveled distance (p < 0.0001), enclosed area (p < 0.002), eccentricity (p < 0.02), and peak displacement (p < 0.02) of the endocardial center. Patients with positive late gadolinium enhancement showed a higher variability of ECM measures across different slices (p < 0.05). SIGNIFICANCE: ECM analysis is feasible and it allows the assessment of left ventricular mechanical discoordination. Differences in ECM measures permit one to distinguish between LBBB and healthy volunteers.

Assessment of left ventricular mechanical dyssynchrony in left bundle branch block canine model: Comparison between cine and tagged MRI.

PURPOSE: To compare cine and tagged magnetic resonance imaging (MRI) for left ventricular dyssynchrony assessment in left bundle branch block (LBBB), using the time-to-peak contraction timing, and a novel approach based on cross-correlation. MATERIALS AND METHODS: We evaluated a canine model dataset (n = 10) before (pre-LBBB) and after induction of isolated LBBB (post-LBBB). Multislice short-axis tagged and cine MRI images were acquired using a 1.5 T scanner. We computed contraction time maps by cross-correlation, based on the timing of radial wall motion and of circumferential strain. Finally, we estimated dyssynchrony as the standard deviation of the contraction time over the different regions of the myocardium. RESULTS: Induction of LBBB resulted in a significant increase in dyssynchrony (cine: 13.0 ± 3.9 msec for pre-LBBB, and 26.4 ± 5.0 msec for post-LBBB, P = 0.005; tagged: 17.1 ± 5.0 msec at for pre-LBBB, and 27.9 ± 9.8 msec for post-LBBB, P = 0.007). Dyssynchrony assessed by cine and tagged MRI were in agreement (r = 0.73, P = 0.0003); differences were in the order of time difference between successive frames of 20 msec (bias: -2.9 msec; limit of agreement: 10.1 msec). Contraction time maps were derived; agreement was found in the contraction patterns derived from cine and tagged MRI (mean difference in contraction time per segment: 3.6 ± 13.7 msec). CONCLUSION: This study shows that the proposed method is able to quantify dyssynchrony after induced LBBB in an animal model. Cine-assessed dyssynchrony agreed with tagged-derived dyssynchrony, in terms of magnitude and spatial direction. J. MAGN. RESON. IMAGING 2016;44:956-963.

Automatic quantitative left ventricular analysis of cine MR images by using three-dimensional information for contour detection.

The purpose of this study was to evaluate an automatic boundary detection algorithm of the left ventricle on magnetic resonance (MR) short-axis images with the essential restriction of no manual corrections. The study comprised 13 patients (nine men, four women) and 12 healthy volunteers (11 men, one woman), and institutional review board approval and informed consent were obtained. The outline of the left ventricle was indicated manually on horizontal and vertical long-axis MR images. The calculated intersection points with the short-axis MR images were the basis of the automatic contour detection. Automatically derived volumes correlated highly with manually derived (short axis-based) volumes (R2 = 0.98); ejection fraction (EF) and mass showed a correlation of 0.95 and 0.93, respectively. Automatic contour detection reduced interobserver variability to 0.1 mL for endocardial end-diastolic and end-systolic volumes, 1.1 mL for epicardial end-diastolic and end-systolic volumes, 0.02% for EF, and 1.1 g for mass. Thus, the algorithm enabled highly reproducible left ventricular parameters to be obtained.