In vivo validation of cardiac output assessment in non-standard 3D echocardiographic images

Automatic segmentation of the endocardial surface in three-dimensional (3D) echocardiographic images is an important tool to assess left ventricular (LV) geometry and cardiac output (CO). The presence of speckle noise as well as the nonisotropic characteristics of the myocardium impose strong demands on the segmentation algorithm. In the analysis of normal heart geometries of standardized (apical) views, it is advantageous to incorporate a priori knowledge about the shape and appearance of the heart. In contrast, when analyzing abnormal heart geometries, for example in children with congenital malformations, this a priori knowledge about the shape and anatomy of the LV might induce erroneous segmentation results. This study describes a fully automated segmentation method for the analysis of non-standard echocardiographic images, without making strong assumptions on the shape and appearance of the heart. The method was validated in vivo in a piglet model. Real-time 3D echocardiographic image sequences of five piglets were acquired in radiofrequency (rf) format. These ECG-gated full volume images were acquired intra-operatively in a non-standard view. Cardiac blood flow was measured simultaneously by an ultrasound transit time flow probe positioned around the common pulmonary artery. Three-dimensional adaptive filtering using the characteristics of speckle was performed on the demodulated rf data to reduce the influence of speckle noise and to optimize the distinction between blood and myocardium. A gradient-based 3D deformable simplex mesh was then used to segment the endocardial surface. A gradient and a speed force were included as external forces of the model. To balance data fitting and mesh regularity, one fixed set of weighting parameters of internal, gradient and speed forces was used for all data sets. End-diastolic and end-systolic volumes were computed from the segmented endocardial surface. The cardiac output derived from this automatic segmentation was validated quantitatively by comparing it with the CO values measured from the volume flow in the pulmonary artery. Relative bias varied between 0 and -17%, where the nominal accuracy of the flow meter is in the order of 10%. 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 image segmentation.