Quantitative diagnosis of stress-induced myocardial ischemia using analysis of contrast echocardiographic parametric perfusion images.

Aims: Parametric imaging of myocardial perfusion provides useful visual information for the diagnosis of coronary artery disease (CAD). We developed a technique for automated detection of perfusion defects based on quantitative analysis of parametric perfusion images and validated it against coronary angiography.
Methods and Results: Contrast-enhanced, apical 2-, 3- and 4-chamber images were obtained at rest and with dipyridamole in 34 patients with suspected CAD. Images were analyzed to generate parametric perfusion images of the standard contrast-replenishment model parameters A, beta and A.beta. Each parametric image was divided into six segments, and mean parameter value (MPV) was calculated for each segment. Segmental MPV ratio between stress and rest was defined as a flow reserve index (FRI). Receiver operating characteristics (ROC) analysis was used in a Study group (N=17) to optimize FRI threshold and the minimal number of abnormal segments per vascular territory (LAD and non-LAD), required for automated detection of stress-induced perfusion defects. The optimized detection algorithm was then tested prospectively in the remaining 17 patients (Test group). LAD and non-LAD stenosis >70% was found in 19 and 17 patients, respectively. In the Study group, FRI threshold was: LAD=0.95 and non-LAD=0.68, minimal number of abnormal segments was four and two, correspondingly. Sensitivity, specificity and accuracy in the Test group were: 75%, 67% and 71% in the LAD, and 75%, 75% and 75% in the non-LAD territories.
Conclusion: Automated quantitative analysis of contrast echocardiographic parametric perfusion images is feasible and may aid in the objective detection of CAD.