Real-time three-dimensional echocardiography (RT3DE) is a non-invasive method to visualize the heart. Disadvantageously,
it suffers from non-uniform image quality and a limited field of view. Image quality can be improved
by fusion of multiple echocardiography images. Successful registration of the images is essential for prosperous
fusion. Therefore, this study examines the performance of different methods for intrasubject registration of
multi-view apical RT3DE images.
A total of 14 data sets was annotated by two observers who indicated the position of the apex and four points
on the mitral valve ring. These annotations were used to evaluate registration. Multi-view end-diastolic (ED)
as well as end-systolic (ES) images were rigidly registered in a multi-resolution strategy. The performance of
single-frame and multi-frame registration was examined. Multi-frame registration optimizes the metric for several
time frames simultaneously. Furthermore, the suitability of mutual information (MI) as similarity measure was
compared to normalized cross-correlation (NCC). For initialization of the registration, a transformation that
describes the probe movement was obtained by manually registering five representative data sets.
It was found that multi-frame registration can improve registration results with respect to single-frame registration.
Additionally, NCC outperformed MI as similarity measure. If NCC was optimized in a multi-frame
registration strategy including ED and ES time frames, the performance of the automatic method was comparable
to that of manual registration.
In conclusion, automatic registration of RT3DE images performs as good as manual registration. As registration
precedes image fusion, this method can contribute to improved quality of echocardiography images.
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 using 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.
Automated landmark detection may prove invaluable in the analysis of real-time three-dimensional (3D)
echocardiograms. By detecting 3D anatomical landmark points, the standard anatomical views can be extracted
automatically in apically acquired 3D ultrasound images of the left ventricle, for better standardization of visualization
and objective diagnosis. Furthermore, the landmarks can serve as an initialization for other analysis methods, such as
segmentation. The described algorithm applies landmark detection in perpendicular planes of the 3D dataset. The
landmark detection exploits a large database of expert annotated images, using an extensive set of Haar features for fast
classification. The detection is performed using two cascades of Adaboost classifiers in a coarse to fine scheme. The
method is evaluated by measuring the distance of detected and manually indicated landmark points in 25 patients. The
method can detect landmarks accurately in the four-chamber (apex: 7.9±7.1mm, septal mitral valve point: 5.6±2.7mm;
lateral mitral valve point: 4.0±2.6mm) and two-chamber view (apex: 7.1±6.7mm, anterior mitral valve point:
5.8±3.5mm, inferior mitral valve point: 4.5±3.1mm). The results compare well to those reported by others.
For obtaining quantitative and objective functional parameters from three-dimensional (3D) echocardiographic sequences, automated segmentation methods may be preferable to cumbersome manual delineation of 3D borders. In this study, a novel optical-flow based tracking method is proposed for propagating 3D endocardial contours of the left ventricle throughout the cardiac cycle. To take full advantage of the time-continuous nature of cardiac motion, a statistical motion model was explicitly embedded in the optical flow solution. The cardiac motion was modeled as frame-to-frame affine transforms, which were extracted using Procrustes analysis on a set of training contours. Principal component analysis was applied to obtain a compact model of cardiac motion throughout the whole cardiac cycle. The parameters of this model were resolved in an optical flow manner, via spatial and temporal gradients in image intensity. The algorithm was tested on 36 noncontrast and 28 contrast enhanced 3D echocardiographic sequences in a leave-one-out manner. Good results were obtained using a combination of the proposed motion-guided method and a purely data-driven optical flow approach. The improvement was particularly noticeable in areas where the LV wall was obscured by image artifacts. In conclusion, the results show the applicability of the proposed method in clinical quality echocardiograms.
Automated image processing techniques may prove invaluable in the examination of real-time three-dimensional echocardiograms, by providing quantitative and objective measurements of functional parameters such as left ventricular (LV) volume and ejection fraction. In this study, we investigate the use of active appearance models (AAMs) for automatic detection of left ventricular endocardial contours. AAMs are especially useful in segmenting ultrasound images, due to their ability to model the typical LV appearance. However, since only a limited number of images is available for training, the model may be incapable of capturing the large variability in ultrasound image appearance. This may cause standard AAM matching procedures to fail if the model and image are significantly different. Recently, a Jacobian-tuning method for AAM matching was proposed, which allowed the model's training matrix to adapt to the new, unseen image. This may potentially result in a more robust matching. To compare both matching methods, AAMs were built with end-diastolic images from 54 patients. Larger capture ranges and higher accuracy were obtained when the new method was used. In conclusion, this method has great potential for segmentation in echocardiograms and will improve the assessment of LV functional parameters.
Three-dimensional (3D) stress echocardiography is a novel technique for diagnosing cardiac dysfunction, by comparing wall motion of the left ventricle under different stages of stress. For quantitative comparison of this motion, it is essential to register the ultrasound data. We propose an intensity based rigid registration method to retrieve two-dimensional (2D) four-chamber (4C), two-chamber, and short-axis planes from the 3D data set acquired in the stress stage, using manually selected 2D planes in the rest stage as reference. The algorithm uses the Nelder-Mead simplex optimization to find the optimal transformation of one uniform scaling, three rotation, and three translation parameters. We compared registration using the SAD, SSD, and NCC metrics, performed on four resolution levels of a Gaussian pyramid. The registration's effectiveness was assessed by comparing the 3D positions of the registered apex and mitral valve midpoints and 4C direction with the manually selected results. The registration was tested on data from 20 patients. Best results were found using the NCC metric on data downsampled with factor two: mean registration errors were 8.1mm, 5.4mm, and 8.0° in the apex position, mitral valve position, and 4C direction respectively. The errors were close to the interobserver (7.1mm, 3.8mm, 7.4°) and intraobserver variability (5.2mm, 3.3mm, 7.0°), and better than the error before registration (9.4mm, 9.0mm, 9.9°). We demonstrated that the registration algorithm visually and quantitatively improves the alignment of rest and stress data sets, performing similar to manual alignment. This will improve automated analysis in 3D stress echocardiography.