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Purpose: Color encoding of left ventricular (LV) endocardial motion in 2D echo (2DE) has been shown to improve visual detection of regional wall motion abnormalities (RWMA). Our goals were: 1) to extend this technique to color encode detected LV endocardial surfaces from real-time 3D echo (RT3DE) images; 2) to automatically detect systolic RWMA and test its accuracy against visual interpretation of 2DE images. Methods: 19 normal subjects (N), and 14 patients with RWMA (including 7 with global LV dysfunction) underwent RT3DE and 2DE (Philips). 2DE images (apical 2-, 3and 4-chamber) were reviewed by an expert cardiologist, who graded wall motion (18 segments model) as normal or abnormal (AB). RT3DE datasets were analyzed using custom software: frame-by-frame semiautomated LV surface detection was followed by logical operations applied to each pair of consecutive frames, to track and color encode endocardial motion in 3D. Then, regional fractional volume change (RFVC) in % of regional end-diastolic volume was calculated automatically by colored voxels count, and displayed as stacked color-histograms. In RWMA, RFVCs were compared with regional thresholds, derived from RFVC computed in N and optimized using ROC analysis, for automated classification of wall motion. Results: The generation of 3D color-encoded wall-motion and RFVC quantification took <10 sec. In RWMA, 182/252 segments were graded as AB (Figure 1, arrows). The automated technique agreed with the expert reader in 225/252 (89%) segments, with only 8/252 (3%) false positive and 19/252 (8%) false negative detections (sensitivity: 0.91; specificity: 0.80; accuracy: 0.84). Conclusions: The proposed technique could improve the visual assessment of wall motion from RT3DE images, and represent the basis for automated detection of RWMA.

european journal of echocardiography

611 Surface detection and color-encoding applied to real-time three-dimensional echocardiographic images as a basis for automated assessment of left ventricular wall motion