Volumetric analysis of regional left ventricular function with real-time three-dimensional echocardiography: validation by magnetic resonance and clinical utility testing.

Background: Quantitative information on regional left ventricular volumes from real-time three-dimensional echocardiographic (RT3DE) images has significant clinical potential but needs validation.
Aim: To validate these measurements against cardiac magnetic resonance (CMR) and test the feasibility of automated detection of regional wall motion (RWM) abnormalities from RT3DE data.
Methods: RT3DE (Philips) and CMR (Siemens) images were obtained from 31 patients and analysed by using prototype software to semiautomatically calculate indices of regional left ventricular function, which were compared between RT3DE and CMR (linear regression, Bland-Altman). Additionally, CMR images were reviewed by an expert, whose RWM grades were used as a reference for automated classification of segments as normal or abnormal from RT3DE and from CMR images. For each modality, normal regional ejection fraction (REF) values were obtained from 15 patients with normal wall motion. In the remaining 16 patients, REFs were compared with thresholds that were derived from patients with normal wall motion and optimised using receiver operating characteristic analysis.
Results: RT3DE measurements resulted in good agreement with CMR. Regional indices calculated in patients with normal wall motion varied between segments, but overall were similar between modalities. In patients with abnormal wall motion, RWM was graded as abnormal in 74% segments. CMR and RT3DE thresholds were similar (16-segment average 55 (10)% and 56 (7)%, respectively). Automated interpretation resulted in good agreement with expert interpretation, similar for CMR and RT3DE (sensitivity 0.85, 0.84; specificity 0.81, 0.78; accuracy 0.84, 0.84, respectively).
Conclusion: Analysis of RT3DE data provides accurate quantification of regional left ventricular function and allows semiautomated detection of RWM abnormalities, which is as accurate as the same algorithm applied to CMR images.