Correlation-based discrimination between myocardial tissue and blood in 3D echocardiographic images

The aim of this study was to investigate the merit of using temporal cross-correlation between subsequent echo volumes for discriminating blood and myocardial tissue, to facilitate automated segmentation of 3D echocardiographic images over the entire cardiac cycle. Because of poor echogenicity contrast between blood and myocardial tissue in some regions and the presence of speckle noise, automated segmentation is challenging. For patients with a congenital heart disease, segmentation should rely on echo features solely as incorporation of a priori knowledge of the shape of the heart is undesirable. Therefore, we analyzed the performance of a 3D iterative cross-correlation algorithm to obtain optimal contrast of maximum cross-correlation (MCC) values between blood and myocardial tissue in all phases of the cardiac cycle. Both contrast and boundary-gradient quality measures were assessed to optimize the MCC-values with respect to signal choice (radiofrequency (RF) data or envelope data) and axial window size. Results in 3D echocardiographic image sequences of five healthy children demonstrate that the use of envelope data outperformed the use of RF-data in terms of optimal blood-myocardium CNR, Overlap and Acutance, in all phases of the cardiac cycle (p <; 0.05). The use of a relatively small axial window (0.7 - 1.25 mm) at fine-scale resulted in optimal contrast and boundary-gradient between the two tissues. Optimal MCC-values, either alone or in combination with adaptive filtered, demodulated RF-data, were used as additional external force in a deformable model to segment the left ventricular cavity in one dataset. Incorporation of MCC-values had additional value for automated segmentation.