Selection Strategies for Atlas-Based Mosaicing of Left Atrial 3-D Transesophageal Echocardiography Data.

Three-dimensional transesophageal echocardiography (TEE) provides real-time soft tissue information, but its use is hampered by its limited field of view. The mosaicing of multiple TEE views makes it possible to visualize a large structure, like the left atrium, in a single volume. To this end, an automatic registration method is required. Similarly to atlas-based segmentation approaches, atlas-based mosaicing (ABM) uses a full volume atlas set to moderate the onerous registration of the individual TEE views. The performance of ABM depends both on the quality of the involved registrations and on the selection of the optimal transformation from the candidate transformations that result from the various atlases. The study described here explored the performance of different selection strategies on multiview TEE data of the left atrium. We found that by incorporating two stages of transformation selection, using the image similarity and the conformity between the candidate transformations as selection criteria, the average registration error dropped below 3 mm with respect to manual registration of these data. Finally, we used this method for the automatic construction of a wide-view TEE volume of the left atrium.

Segmentation of multiple heart cavities in 3-D transesophageal ultrasound images.

Three-dimensional transesophageal echocardiography (TEE) is an excellent modality for real-time visualization of the heart and monitoring of interventions. To improve the usability of 3-D TEE for intervention monitoring and catheter guidance, automated segmentation is desired. However, 3-D TEE segmentation is still a challenging task due to the complex anatomy with multiple cavities, the limited TEE field of view, and typical ultrasound artifacts. We propose to segment all cavities within the TEE view with a multi-cavity active shape model (ASM) in conjunction with a tissue/blood classification based on a gamma mixture model (GMM). 3-D TEE image data of twenty patients were acquired with a Philips X7-2t matrix TEE probe. Tissue probability maps were estimated by a two-class (blood/tissue) GMM. A statistical shape model containing the left ventricle, right ventricle, left atrium, right atrium, and aorta was derived from computed tomography angiography (CTA) segmentations by principal component analysis. ASMs of the whole heart and individual cavities were generated and consecutively fitted to tissue probability maps. First, an average whole-heart model was aligned with the 3-D TEE based on three manually indicated anatomical landmarks. Second, pose and shape of the whole-heart ASM were fitted by a weighted update scheme excluding parts outside of the image sector. Third, pose and shape of ASM for individual heart cavities were initialized by the previous whole heart ASM and updated in a regularized manner to fit the tissue probability maps. The ASM segmentations were validated against manual outlines by two observers and CTA derived segmentations. Dice coefficients and point-to-surface distances were used to determine segmentation accuracy. ASM segmentations were successful in 19 of 20 cases. The median Dice coefficient for all successful segmentations versus the average observer ranged from 90% to 71% compared with an inter-observer range of 95% to 84%. The agreement against the CTA segmentations was slightly lower with a median Dice coefficient between 85% and 57%. In this work, we successfully showed the accuracy and robustness of the proposed multi-cavity segmentation scheme. This is a promising development for intraoperative procedure guidance, e.g., in cardiac electrophysiology.

Improved Segmentation of Multiple Cavities of the Heart in Wide-View 3-D Transesophageal Echocardiograms.

Minimally invasive interventions in the heart such as in electrophysiology are becoming more and more important in clinical practice. Currently, preoperative computed tomography angiography (CTA) is used to provide anatomic information during electrophysiology interventions, but this does not provide real-time feedback and burdens the patient with additional radiation and side effects of the contrast agent. Three-dimensional transesophageal echocardiography (TEE) is an excellent modality for visualization of anatomic structures and instruments in real time, but some cavities, especially the left atrium, suffer from the limited coverage of the 3-D TEE volumes. This leads to difficulty in segmenting the left atrium. We propose replacing or complementing pre-operative CTA imaging with wide-view TEE. We tested this proposal on 20 patients for which TEE image volumes covering the left atrium and CTA images were acquired. The TEE images were manually registered, and wide-view volumes were generated. Five heart cavities in single-view and wide-view TEE were segmented and compared with atlas based-segmentations derived from the CTA images. We found that the segmentation accuracy (Dice coefficients) improved relative to segmentation of single-view images by 5, 15 and 9 percentage points for the left atrium, right atrium and aorta, respectively. Average anatomic coverage was improved by 2, 29, 62 and 49 percentage points for the right ventricle, left atrium, right atrium and aorta, respectively. This finding confirms that wide-view 3-D TEE can be useful in supporting electrophysiology interventions.

Carotid intraplaque neovascularization quantification software (CINQS).

Intraplaque neovascularization (IPN) is an important biomarker of atherosclerotic plaque vulnerability. As IPN can be detected by contrast enhanced ultrasound (CEUS), imaging-biomarkers derived from CEUS may allow early prediction of plaque vulnerability. To select the best quantitative imaging-biomarkers for prediction of plaque vulnerability, a systematic analysis of IPN with existing and new analysis algorithms is necessary. Currently available commercial contrast quantification tools are not applicable for quantitative analysis of carotid IPN due to substantial motion of the carotid artery, artifacts, and intermittent perfusion of plaques. We therefore developed a specialized software package called Carotid intraplaque neovascularization quantification software (CINQS). It was designed for effective and systematic comparison of sets of quantitative imaging biomarkers. CINQS includes several analysis algorithms for carotid IPN quantification and overcomes the limitations of current contrast quantification tools and existing carotid IPN quantification approaches. CINQS has a modular design which allows integrating new analysis tools. Wizard-like analysis tools and its graphical-user-interface facilitate its usage. In this paper, we describe the concept, analysis tools, and performance of CINQS and present analysis results of 45 plaques of 23 patients. The results in 45 plaques showed excellent agreement with visual IPN scores for two quantitative imaging-biomarkers (The area under the receiver operating characteristic curve was 0.92 and 0.93).

Side-by-side viewing of anatomically aligned left ventricular segments in three-dimensional stress echocardiography.

BACKGROUND: Dobutamine stress echocardiography (DSE) suffers from high interobserver and inter-institution variability in the diagnosis of myocardial ischemia. Therefore, we developed a three-dimensional (3D) analysis tool that makes it possible to anatomically align 3D rest and stress data systematically, to generate optimal, nonforeshortened standard anatomical cross sections and to analyse the images synchronized and side-by-side. AIM OF THE STUDY: To investigate whether this 3D analysis tool could improve interobserver agreement on myocardial ischemia during 3D DSE. METHODS: The study comprised 34 consecutive patients with stable chest pain who underwent both noncontrast and contrast 3D DSE. Two observers scored segmental wall motion using a conventional analysis and the novel analysis with the new 3D tool. RESULTS: The two observers agreed on the presence or absence of myocardial ischemia in 81 of 102 coronary territories (agreement 79%, kappa (kappa) 0.28) during noncontrast 3D imaging and 92 of 102 coronary territories (agreement 90%, kappa 0.65) during contrast-enhanced 3D imaging. With the new 3D analysis software these numbers improved to 98 of 102 coronary territories (agreement 96%, kappa 0.69) during noncontrast 3D imaging and 98 of 102 coronary territories (agreement 96%, kappa 0.82) during contrast-enhanced 3D imaging. CONCLUSION: The use of a 3D DSE analysis tool improves interobserver agreement for myocardial ischemia both for noncontrast and contrast images.

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

RATIONALE AND OBJECTIVES: We propose a semiautomatic endocardial border detection method for three-dimensional (3D) time series of cardiac ultrasound (US) data based on pattern matching and dynamic programming, operating on two-dimensional (2D) slices of the 3D plus time data, for the estimation of full cycle left ventricular volume, with minimal user interaction. MATERIALS AND METHODS: The presented method is generally applicable to 3D US data and evaluated on data acquired with the Fast Rotating Ultrasound (FRU-) Transducer, developed by Erasmus Medical Center (Rotterdam, the Netherlands), a conventional phased-array transducer, rotating at very high speed around its image axis. The detection is based on endocardial edge pattern matching using dynamic programming, which is constrained by a 3D plus time shape model. It is applied to an automatically selected subset of 2D images of the original data set, for typically 10 equidistant rotation angles and 16 cardiac phases (160 images). Initialization requires the drawing of four contours per patient manually. We evaluated this method on 14 patients against MRI end-diastole and end-systole volumes. Initialization requires the drawing of four contours per patient manually. We evaluated this method on 14 patients against MRI end-diastolic (ED) and end-systolic (ES) volumes. RESULTS: The semiautomatic border detection approach shows good correlations with MRI ED/ES volumes (r = 0.938) and low interobserver variability (y = 1.005x - 16.7, r = 0.943) over full-cycle volume estimations. It shows a high consistency in tracking the user-defined initial borders over space and time. CONCLUSIONS: We show that the ease of the acquisition using the FRU-transducer and the semiautomatic endocardial border detection method together can provide a way to quickly estimate the left ventricular volume over the full cardiac cycle using little user interaction.