Echocardiographic Quantification of Regional Left Ventricular Wall Motion With Color Kinesis

BACKGROUND: Color kinesis is a new technology for the echocardiographic assessment of left ventricular wall motion based on acoustic quantification. This technique automatically detects endocardial motion in real time by using integrated backscatter data to identify pixel transitions from blood to tissue during systole on a frame-by-frame basis. In this study, we evaluated the feasibility and accuracy of quantitative segmental analysis of color kinesis images to provide objective evaluation of regional systolic endocardial motion. METHODS AND RESULTS: Two-dimensional echocardiograms were obtained in the short-axis and apical four-chamber views in 20 normal subjects and 40 patients with regional wall motion abnormalities. End-systolic color overlays superimposed on the gray scale images were obtained with color kinesis to color encode left ventricular endocardial motion throughout systole on a frame-by-frame basis. These color-encoded images were divided into segments by use of custom software. In each segment, pixels of different colors were counted and displayed as stacked histograms reflecting the magnitude and timing of regional endocardial excursion. In normal subjects, histograms were found to be highly consistent and reproducible. The patterns of contraction obtained in normal subjects were used as a reference for the objective automated interpretation of regional wall motion abnormalities, defined as deviations from this pattern. The variability in the echocardiographic interpretation of wall motion between two experienced readers was similar to the diagnostic variability between the consensus of the two readers and the automated interpretation. CONCLUSIONS: Color kinesis is a promising new tool that may be used clinically to improve the qualitative and quantitative evaluation of spatial and temporal aspects of global and regional wall motion. In this initial study, segmental analysis of color kinesis images provided accurate, automated, and quantitative diagnosis of regional wall motion abnormalities.