Left ventricular volume estimation in cardiac three-dimensional ultrasound: a semiautomatic border detection approach

New developments in paediatric cardiac functional ultrasound imaging

Ultrasound imaging can be used to estimate the morphology as well as the motion and deformation of tissues. If the interrogated tissue is actively deforming, this deformation is directly related to its function and quantification of this deformation is normally referred as 'strain imaging'. Tissue can also be deformed by applying an internal or external force and the resulting, induced deformation is a function of the mechanical tissue characteristics. In combination with the load applied, these strain maps can be used to estimate or reconstruct the mechanical properties of tissue. This technique was named 'elastography' by Ophir et al. in 1991. Elastography can be used for atherosclerotic plaque characterisation, while the contractility of the heart or skeletal muscles can be assessed with strain imaging. Rather than using the conventional video format (DICOM) image information, radio frequency (RF)-based ultrasound methods enable estimation of the deformation at higher resolution and with higher precision than commercial methods using Doppler (tissue Doppler imaging) or video image data (2D speckle tracking methods). However, the improvement in accuracy is mainly achieved when measuring strain along the ultrasound beam direction, so it has to be considered a 1D technique. Recently, this method has been extended to multiple directions and precision further improved by using spatial compounding of data acquired at multiple beam steered angles. Using similar techniques, the blood velocity and flow can be determined. RF-based techniques are also beneficial for automated segmentation of the ventricular cavities. In this paper, new developments in different techniques of quantifying cardiac function by strain imaging, automated segmentation, and methods of performing blood flow imaging are reviewed and their application in paediatric cardiology is discussed.

Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data

Clinical diagnosis of heart disease might be substantially supported by automated segmentation of the endocardial surface in three-dimensional (3-D) echographic images. Because of the poor echogenicity contrast between blood and myocardial tissue in some regions and the inherent speckle noise, automated analysis of these images is challenging. A priori knowledge on the shape of the heart cannot always be relied on, e.g., in children with congenital heart disease, segmentation should be based on the echo features solely. The objective of this study was to investigate the merit of using temporal cross-correlation of radio-frequency (RF) data for automated segmentation of 3-D echocardiographic images. Maximum temporal cross-correlation (MCC) values were determined locally from the RF-data using an iterative 3-D technique. MCC values as well as a combination of MCC values and adaptive filtered, demodulated RF-data were used as an additional, external force in a deformable model approach to segment the endocardial surface and were tested against manually segmented surfaces. Results on 3-D full volume images (Philips, iE33) of 10 healthy children demonstrate that MCC values derived from the RF signal yield a useful parameter to distinguish between blood and myocardium in regions with low echogenicity contrast and incorporation of MCC improves the segmentation results significantly. Further investigation of the MCC over the whole cardiac cycle is required to exploit the full benefit of it for automated segmentation.

Left ventricular border tracking using cardiac motion models and optical flow

The use of automated methods is becoming increasingly important for assessing cardiac function quantitatively and objectively. In this study, we propose a method for tracking three-dimensional (3-D) left ventricular contours. The method consists of a local optical flow tracker and a global tracker, which uses a statistical model of cardiac motion in an optical-flow formulation. We propose a combination of local and global trackers using gradient-based weights. The algorithm was tested on 35 echocardiographic sequences, with good results (surface error: 1.35 ± 0.46 mm, absolute volume error: 5.4 ± 4.8 mL). This demonstrates the method's potential in automated tracking in clinical quality echocardiograms, facilitating the quantitative and objective assessment of cardiac function.

Automated border detection in three-dimensional echocardiography: principles and promises

Several automated border detection approaches for three-dimensional echocardiography have been developed in recent years, allowing quantification of a range of clinically important parameters. In this review, the background and principles of these approaches and the different classes of methods are described from a practical perspective, as well as the research trends to achieve a robust method.

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.

Automated tracking of the mitral valve annulus motion in apical echocardiographic images using multidimensional dynamic programming

We developed a semiautomatic method for tracking the mitral valve annulus (MVA) in echocardiographic images, in particular, tracking the septal and the lateral mitral valve hinge points. The algorithm is based on multidimensional dynamic programming combined with apodized block matching. The method was tested on single-beat apical four chamber image sequences of 20 patients with acute myocardial infarction. The automated tracking results were evaluated by comparing them with the average manual tracking results of two experts. The mitral valve hinge point displacements and the total mitral excursions obtained by the automatic technique agreed well with those obtained manually and outperformed two commonly used tracking methods (forward tracking and minimum tracking). In conclusion, this novel semiautomatic tracking method is clinically valuable and capable of tracking the MVA motion within the limits of interobserver variability. The technique is robust, even in low frame rate, redigitized VCR images of clinical quality.