Time continuous detection of the left ventricular long axis and the mitral valve plane in 3-D echocardiography.

Automated segmentation approaches for the left ventricle (LV) in 3-D echocardiography (3DE) often rely on manual initialization. So far, little effort has been put into automating the initialization procedure to get to a fully automatic segmentation approach. We propose a fully automatic method for the detection of the LV long axis (LAX) and the mitral valve plane (MVP) over the full cardiac cycle, for the initialization of segmentation algorithms in 3DE. Our method exploits the cyclic motion of the LV and therefore detects salient structures in a time-continuous way. Probabilities to candidate LV center points are assigned through a Hough transform for circles. The LV LAX is detected by combining dynamic programming detections on these probabilities in 3-D and 2D + time to obtain a time continuous solution. Subsequently, the mitral valve plane is detected in a projection of the data on a plane through the previously detected LAX. The method easily adjusts to different acquisition routines and combines robustness with good accuracy and low computational costs. Automatic detection was evaluated using patient data acquired with the fast rotating ultrasound (FRU) transducer (n=11 patients) and with the Philips Sonos 7500 ultrasound system (Philips Medical Systems, Andover, MA, USA), with the X4 matrix transducer (n=14 patients). For the FRU-transducer data, the LAX was estimated with a distance error of 2.85+/-1.70 mm (mean+/-SD) and an angle of 5.25+/-3.17 degrees; the mitral valve plane was estimated with a distance of -1.54+/-4.31 mm. For the matrix data, these distances were 1.96+/-1.30 mm with an angle error of 5.95+/-2.11 and -1.66+/-5.27 mm for the mitral valve plane. These results confirm that the method is very suitable for automatic detection of the LV LAX and MVP. It provides a basis for further automatic exploration of the LV and could therefore serve as a replacement of manual initialization of 3-D segmentation approaches.