K. Y. Esther Leung

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.

Motion compensation for intravascular ultrasound palpography

Rupture of vulnerable plaques in coronary arteries is the major cause of acute coronary syndromes. Most vulnerable plaques consist of a thin fibrous cap covering an atheromous core. These plaques can be identified using intravascular ultrasound (IVUS) palpography, which measures radial strain by cross-correlating RF signals at different intraluminal pressures. Multiple strain images (i.e., partial palpograms) are averaged per heart cycle to produce a more robust compounded palpogram. However, catheter motion due to cardiac activity causes misalignment of the RF signals and thus of the partial palpograms, resulting in less valid strain estimates. To compensate for in-plane catheter rotation and translation, we devised four methods based on block matching. The global rotation block matching (GRBM) and contour mapping (CMAP) methods measure catheter rotation, and local block matching (LBM) and catheter rotation and translation (CRT) estimate displacements of local tissue regions. These methods were applied to nine in vivo pullback acquisitions, made with a 20 MHz phased-array transducer. We found that all these methods significantly increase the number of valid strain estimates in the partial and compounded palpograms (P < 0.008). The best method, LBM, attained an average increase of 17% and 15%, respectively. Implementation of this method should improve the information coming from IVUS palpography, leading to better vulnerable plaque detection.

Sparse Registration for Three-Dimensional Stress Echocardiography

Three-dimensional (3-D) stress echocardiography is a novel technique for diagnosing cardiac dysfunction. It involves evaluating wall motion of the left ventricle, by visually analyzing ultrasound images obtained in rest and in different stages of stress. Since the acquisitions are performed minutes apart, variabilities may exist in the visualized cross-sections. To improve anatomical correspondence between rest and stress, aligning the images is essential. We developed a new intensity-based, sparse registration method to retrieve standard anatomical views from 3-D stress images that were equivalent to the manually selected views in the rest images. Using sparse image planes, the influence of common image artifacts could be reduced. We investigated different similarity measures and different levels of sparsity. The registration was tested using data of 20 patients and quantitatively evaluated based on manually defined anatomical landmarks. Alignment was best using sparse registration with two long-axis and two short-axis views; registration errors were reduced significantly, to the range of interobserver variabilities. In 91% of the cases, the registration result was qualitatively assessed as better than or equal to the manual alignment. In conclusion, sparse registration improves the alignment of rest and stress images, with a performance similar to manual alignment. This is an important step towards objective quantification in 3-D stress echocardiography.

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 methods potential in automated tracking in clinical quality echocardiograms, facilitating the quantitative and objective assessment of cardiac function.

Localized shape variations for classifying wall motion in echocardiograms

To quantitatively predict coronary artery diseases, automated analysis may be preferred to current visual assessment of left ventricular (LV) wall motion. In this paper, a novel automated classification method is presented which uses shape models with localized variations. These sparse shape models were built from four-chamber and two-chamber echocardiographic sequences using principal component analysis and orthomax rotations. The resulting shape parameters were then used to classify local wall-motion abnormalities of LV segments. Various orthomax criteria were investigated. In all cases, higher classification correctness was achieved using significantly less shape parameters than before rotation. Since pathologies are typically spatially localized, many medical applications involving local classification should benefit from orthomax parameterizations.

Automatic active appearance model segmentation of 3D echocardiograms

A fully automated segmentation for 3D echocardiography (3DE) using 3D Active Appearance Models (AAM) was developed and evaluated on 99 patients. The method used ultrasound specific grey value normalization and two matching algorithms were tested. To our knowledge this is the first report on a fully operational 3D AAM employed in 3DE on a large scale. The 3D AAM detected the endocardial contours accurately, even in the presence of large variations in left ventricular appearance and shape. Matching was successful in 91% of patients and resulted in a median point-tosurface error of 2.69 mm (av±sd: 2.91±1.03mm). Results indicate that fully automated AAM analysis of 3DE is practically feasible in datasets of mixed origin and quality.

Probabilistic framework for tracking in artifact-prone 3D echocardiograms

The analysis of echocardiograms, whether visual or automated, is often hampered by ultrasound artifacts which obscure the moving myocardial wall. In this study, a probabilistic framework for tracking the endocardial surface in 3D ultrasound images is proposed, which distinguishes between visible and artifact-obscured myocardium. Motion estimation of visible myocardium relies more on a local, data-driven tracker, whereas tracking of obscured myocardium is assisted by a global, statistical model of cardiac motion. To make this distinction, the expectation-maximization algorithm is applied in a stationary and dynamic frame-of-reference. Evaluation on 35 three-dimensional echocardiographic sequences shows that this artifact-aware tracker gives better results than when no distinction is made. In conclusion, the proposed tracker is able to reduce the influence of artifacts, potentially improving quantitative analysis of clinical quality echocardiograms.

Segmental Wall Motion Classification in Echocardiograms Using Compact Shape Descriptors

RATIONALE AND OBJECTIVES Parametric shape representations of endocardial contours, obtained with principal component analysis (PCA) and the orthomax criterion, provide compact descriptors for classifying segmental left ventricular wall motion. MATERIALS AND METHODS Endocardial contours were delineated in the left ventricular echocardiograms of 129 patients. Parametric models of these shapes were built with PCA and subsequently rotated using the orthomax criterion, producing models with local variations. Shape parameters of this localized model were used to predict the presence of wall motion abnormalities, as determined by expert visual wall motion scoring. RESULTS Best results were obtained using the varimax criterion and full variance models. Although traditional PCA models needed 8.0 +/- 3.0 parameters to classify segmental wall motion, only 5.1 +/- 3.2 parameters were needed using the orthomax rotated models (P < .05) to achieve similar classification accuracy. The classification space was also better behaved. CONCLUSIONS Orthomax rotation generates more local parameters, which are successful in reducing the complexity of wall motion classification. Because pathologies are typically spatially localized, many medical applications involving local classification should benefit from orthomax parameterizations.

Local wall-motion classification in echocardiograms using shape models and orthomax rotations

Automating the analysis of left ventricular (LV) wall motion can improve objective prediction of coronary artery disease. A new method for classifying LV wall motion using shape models with localized variations was developed for this purpose. These sparse shape models were built from four-chamber and two-chamber echocardiographic sequences using principal component analysis and orthomax rotations. The resulting shape parameters were then used to classify wall-motion abnormalities of LV segments. Compared with the shape model before rotation, higher classification correctness was achieved using significantly less shape parameters. The local variations exhibited by these shape parameters correlated reasonably with the location of the segments

Multiframe registration of real-time three-dimensional echocardiography time series

Abstract. Mosaicing of real-time three-dimensional echocardiography (RT3-DE) images aims at extending the field-of-view of overlapping images. Currently available methods discard most of the temporal information available in the time series. We investigate the added value of simultaneous registration of multiple temporal frames using common similarity metrics. We combine RT3-DE images of the left and right ventricles by registration and fusion. The standard approach of registering single frames, either end-diastolic (ED) or end-systolic (ES), is compared with simultaneous registration of multiple time frames, to evaluate the effect of using the information from all images in the metric. A transformation estimating the protocol-specific misalignment is used to initialize the registration. It is shown that multiframe registration can be as accurate as alignment of the images based on manual annotations. Multiframe registration using normalized cross-correlation outperforms any of the single-frame methods. As opposed to expectations, extending the multiframe registration beyond simultaneous use of ED and ES frames does not further improve registration results.