Cardiac output estimation in non-standard 3D echocardiographic images

The aim of this study was to validate cardiac output (CO) values obtained by fully automated segmentation of the left ventricle (LV) against CO measured from the volume flow in the pulmonary artery. Segmentation of the LV in 3D echographic images may substantially support clinical diagnosis of (congenital) heart disease. Assumptions about shape and appearance of the heart are often incorporated in the segmentation method. This is advantageous when analyzing echographic images of normal heart geometries in standardized (apical) views. In abnormal heart geometries, for example in children with congenital malformations, this a priori knowledge about the shape and anatomy of the LV will result in erroneous segmentation results. Therefore, we developed an automated segmentation method without using a priori knowledge. Echocardiographic image sequences of five piglets were obtained in radiofrequency (rf) format. Cardiac blood flow was measured simultaneously in the pulmonary artery by an ultrasound flow probe. Three-dimensional adaptive filtering was performed on the demodulated rf-data to optimize the distinction between blood and myocardium. A 3D gradient-based deformable simplex mesh was then used to segment the endocardial surface. The method can be applied to non-standard heart geometries without having to impose shape constraints. Assuming the CO measurements from the flow probe as a gold standard, excellent correlation (r = 0.99) was observed with the CO estimates obtained from segmentation.