Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: a clinical study on right and left ventricles

Echocardiographic image multi-structure segmentation using Cardiac-SegNet

PURPOSE

Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise of the echocardiographic images. Manual segmentation is subject to interobserver variability and is too slow for real-time image-guided interventions. We aim to develop a deep learning-based method for automated multi-structure segmentation of echocardiographic images.

METHODS

We developed an anchor-free mask convolutional neural network (CNN), termed Cardiac-SegNet, which consists of three subnetworks, that is, a backbone, a fully convolutional one-state object detector (FCOS) head, and a mask head. The backbone extracts multi-level and multi-scale features from endocardium image. The FOCS head utilizes these features to detect and label the region-of-interests (ROIs) of the segmentation targets. Unlike the traditional mask regional CNN (Mask R-CNN) method, the FCOS head is anchor-free and can model the spatial relationship of the targets. The mask head utilizes a spatial attention strategy, which allows the network to highlight salient features to perform segmentation on each detected ROI. For evaluation, we investigated 450 patient datasets by a five-fold cross-validation and a hold-out test. The endocardium (LVEndo ) and epicardium (LVEpi ) of the left ventricle and left atrium (LA) were segmented and compared with manual contours using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean absolute distance (MAD), and center-of-mass distance (CMD).

RESULTS

Compared to U-Net and Mask R-CNN, our method achieved higher segmentation accuracy and fewer erroneous speckles. When our method was evaluated on a separate hold-out dataset at the end diastole (ED) and the end systole (ES) phases, the average DSC were 0.952 and 0.939 at ED and ES for the LVEndo , 0.965 and 0.959 at ED and ES for the LVEpi , and 0.924 and 0.926 at ED and ES for the LA. For patients with a typical image size of 549 × 788 pixels, the proposed method can perform the segmentation within 0.5 s.

CONCLUSION

We proposed a fast and accurate method to segment echocardiographic images using an anchor-free mask CNN.

Three-Dimensional Model-Based Segmentation in Echocardiography Using High Temporal Tissue and Blood Flow Information

Accurate 3-D surface segmentation is a challenging task in echocardiography because of the relatively low image quality. We introduce a new method for 3-D segmentation of the endocardium involving temporal decorrelation of echo signals originating from tissue and blood using radiofrequency (RF) signals acquired in 3-D Doppler mode. Temporal features were extracted in 3-D Doppler mode, where a sequence of RF lines is recorded for each image line. Each set of RF lines is highly correlated because of the high pulse repetition frequency. However, for high blood flow, the RF signals will decorrelate over time in contrast to the endocardium, which will remain relatively highly correlated over time. These decorrelation features permit differentiation between myocardial tissue and blood flow. We describe an implementation of a 3-D segmentation model in which temporal information is used as external constraint. The model was validated in a phantom and in vivo in healthy volunteers (n = 5). The phantom study revealed that the model successfully segmented the artificial blood lumen even for low flow velocity and illustrated the sensitivity of the segmentations to flow rate. In healthy volunteers, high Dice similarity indices indicate that 3-D segmentation of the endocardial border in vivo is feasible.

Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces

With the advancement of three-dimensional (3-D) real-time echocardiography in recent years, automatic creation of patient specific geometric models is becoming feasible and important in clinical decision making. However, the vast majority of echocardiographic segmentation methods presented in the literature focus on the left ventricle (LV) endocardial border, leaving segmentation of the right ventricle (RV) a largely unexplored problem, despite the increasing recognition of the RV's role in cardiovascular disease. We present a method for coupled segmentation of the endo- and epicardial borders of both the LV and RV in 3-D ultrasound images. To solve the segmentation problem, we propose an extension of a successful state-estimation segmentation framework with a geometrical representation of coupled surfaces, as well as the introduction of myocardial incompressibility to regularize the segmentation. The method was validated against manual measurements and segmentations in images of 16 patients. Mean absolute distances of [Formula: see text], [Formula: see text], and [Formula: see text] between the proposed and reference segmentations were observed for the LV endocardium, RV endocardium, and LV epicardium surfaces, respectively. The method was computationally efficient, with a computation time of [Formula: see text].

Review of ultrasound image guidance in external beam radiotherapy part II: intra-fraction motion management and novel applications

Imaging has become an essential tool in modern radiotherapy (RT), being used to plan dose delivery prior to treatment and verify target position before and during treatment. Ultrasound (US) imaging is cost-effective in providing excellent contrast at high resolution for depicting soft tissue targets apart from those shielded by the lungs or cranium. As a result, it is increasingly used in RT setup verification for the measurement of inter-fraction motion, the subject of Part I of this review (Fontanarosa et al 2015 Phys. Med. Biol. 60 R77-114). The combination of rapid imaging and zero ionising radiation dose makes US highly suitable for estimating intra-fraction motion. The current paper (Part II of the review) covers this topic. The basic technology for US motion estimation, and its current clinical application to the prostate, is described here, along with recent developments in robust motion-estimation algorithms, and three dimensional (3D) imaging. Together, these are likely to drive an increase in the number of future clinical studies and the range of cancer sites in which US motion management is applied. Also reviewed are selections of existing and proposed novel applications of US imaging to RT. These are driven by exciting developments in structural, functional and molecular US imaging and analytical techniques such as backscatter tissue analysis, elastography, photoacoustography, contrast-specific imaging, dynamic contrast analysis, microvascular and super-resolution imaging, and targeted microbubbles. Such techniques show promise for predicting and measuring the outcome of RT, quantifying normal tissue toxicity, improving tumour definition and defining a biological target volume that describes radiation sensitive regions of the tumour. US offers easy, low cost and efficient integration of these techniques into the RT workflow. US contrast technology also has potential to be used actively to assist RT by manipulating the tumour cell environment and by improving the delivery of radiosensitising agents. Finally, US imaging offers various ways to measure dose in 3D. If technical problems can be overcome, these hold potential for wide-dissemination of cost-effective pre-treatment dose verification and in vivo dose monitoring methods. It is concluded that US imaging could eventually contribute to all aspects of the RT workflow.

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

Automated Assessment of Right Ventricular Volumes and Function Using Three-Dimensional Transesophageal Echocardiography

Assessment of right ventricular (RV) function is known to be of diagnostic value in patients with RV dysfunction. Because of its complex anatomic shape, automated determination of the RV volume is difficult and strong reliance on geometric assumptions is not desired. A method for automated RV assessment was developed using three-dimensional (3-D) echocardiography without relying on a priori knowledge of the cardiac anatomy. A 3-D adaptive filtering technique that optimizes the discrimination between blood and myocardium was applied to facilitate endocardial border detection. Filtered image data were incorporated in a segmentation model to automatically detect the endocardial RV border. End-systolic and end-diastolic RV volumes, as well as ejection fraction, were computed from the automatically segmented endocardial surfaces and compared against reference volumes manually delineated by two expert cardiologists. The results reported good performance in terms of correlation and agreement with the results from the reference volumes.

Automated Segmentation of the Right Ventricle in 3D Echocardiography: A Kalman Filter State Estimation Approach

As the right ventricle's (RV) role in cardiovascular diseases is being more widely recognized, interest in RV imaging, function and quantification is growing. However, there are currently few RV quantification methods for 3D echocardiography presented in the literature or commercially available. In this paper we propose an automated RV segmentation method for 3D echocardiographic images. We represent the RV geometry by a Doo-Sabin subdivision surface with deformation modes derived from a training set of manual segmentations. The segmentation is then represented as a state estimation problem and solved with an extended Kalman filter by combining the RV geometry with a motion model and edge detection. Validation was performed by comparing surface-surface distances, volumes and ejection fractions in 17 patients with aortic insufficiency between the proposed method, magnetic resonance imaging (MRI), and a manual echocardiographic reference. The algorithm was efficient with a mean computation time of 2.0 s. The mean absolute distances between the proposed and manual segmentations were 3.6 ± 0.7 mm. Good agreements of end diastolic volume, end systolic volume and ejection fraction with respect to MRI ( -26±24 mL , -16±26 mL and 0 ± 10%, respectively) and a manual echocardiographic reference (7 ± 30 mL, 13 ± 17 mL and -5±7% , respectively) were observed.

A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset

This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

3-D visualization and non-linear tissue classification of breast tumors using ultrasound elastography in vivo

The goal of the study described here was to introduce new methods for the classification and visualization of human breast tumors using 3-D ultrasound elastography. A tumor's type, shape and size are key features that can help the physician to decide the sort and extent of necessary treatment. In this work, tumor type, being either benign or malignant, was classified non-invasively for nine volunteer patients. The classification was based on estimating four parameters that reflect the tumor's non-linear biomechanical behavior, under multi-compression levels. Tumor prognosis using non-linear elastography was confirmed with biopsy as a gold standard. Three tissue classification parameters were found to be statistically significant with a p-value < 0.05, whereas the fourth non-linear parameter was highly significant, having a p-value < 0.001. Furthermore, each breast tumor's shape and size were estimated in vivo using 3-D elastography, and were enhanced using interactive segmentation. Segmentation with level sets was used to isolate the stiff tumor from the surrounding soft tissue. Segmentation also provided a reliable means to estimate tumors volumes. Four volumetric strains were investigated: the traditional normal axial strain, the first principal strain, von Mises strain and maximum shear strain. It was noted that these strains can provide varying degrees of boundary enhancement to the stiff tumor in the constructed elastograms. The enhanced boundary improved the performance of the segmentation process. In summary, the proposed methods can be employed as a 3-D non-invasive tool for characterization of breast tumors, and may provide early prognosis with minimal pain, as well as diminish the risk of late-stage breast cancer.

Relationship between echocardiographic and magnetic resonance derived measures of right ventricular size and function in patients with pulmonary hypertension

BACKGROUND

Transthoracic echocardiographic (TTE) imaging is the mainstay of clinical practice for evaluating right ventricular (RV) size and function, but its accuracy in patients with pulmonary hypertension has not been well validated.

METHODS

Magnetic resonance imaging (MRI) and TTE images were retrospectively reviewed in 40 consecutive patients with pulmonary hypertension. RV and left ventricular volumes and ejection fractions were calculated using MRI. TTE areas and indices of RV ejection fraction (RVEF) were compared.

RESULTS

The average age was 42 ± 12 years, with a majority of women (85%). There was a wide range of mean pulmonary arterial pressures (27-81 mm Hg) and RV end-diastolic volumes (111-576 mL), RVEFs (8%-67 %), and left ventricular ejection fractions (26%-72%) by MRI. There was a strong association between TTE and MRI-derived parameters: RV end-diastolic area (by TTE imaging) and RV end-diastolic volume (by MRI), R(2) = 0.78 (P < .001); RV fractional area change by TTE imaging and RVEF by MRI, R(2) = 0.76 (P < .001); and tricuspid annular plane systolic excursion by TTE imaging and RVEF by MRI, R(2) = 0.64 (P < .001). By receiver operating characteristic curve analysis, an RV fractional area change < 25% provided excellent discrimination of moderate systolic dysfunction (RVEF < 35%), with an area under the curve of 0.97 (P < .001). An RV end-diastolic area index of 18 cm(2)/m(2) provided excellent discrimination for moderate RV enlargement (area under the curve, 0.89; P < .001).

CONCLUSIONS

Echocardiographic estimates of RV volume (by RV end-diastolic area) and function (by RV fractional area change and tricuspid annular plane systolic excursion) offer good approximations of RV size and function in patients with pulmonary hypertension and allow the accurate discrimination of normal from abnormal.

An integrated system for the segmentation of atherosclerotic carotid plaque ultrasound video

The robust border identification of atherosclerotic carotid plaque, the corresponding degree of stenosis of the common carotid artery (CCA), and also the characteristics of the arterial wall, including plaque size, composition, and elasticity, have significant clinical relevance for the assessment of future cardiovascular events. To facilitate the follow-up and analysis of the carotid stenosis in serial clinical investigations, we propose and evaluate an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA based on video frame normalization, speckle reduction filtering, M-mode state-based identification, parametric active contours, and snake segmentation. Initially, the cardiac cycle in each video is identified and the video M-mode is generated, thus identifying systolic and diastolic states. The video is then segmented for a time period of at least one full cardiac cycle. The algorithm is initialized in the first video frame of the cardiac cycle, with human assistance if needed, and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. Two different initialization methods are investigated in which initial contours are estimated every 20 video frames. In the first initialization method, the initial snake contour is estimated using morphology operators; in the second initialization method, the Chan-Vese active contour model is used. The performance of the algorithm is evaluated on 43 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, available every 20 frames in a time span of 3 to 5 s, covering, in general, 2 cardiac cycles. The segmentation results were very satisfactory, according to the expert objective evaluation, for the two different methods investigated, with true-negative fractions (TNF-specificity) of 83.7 ± 7.6% and 84.3 ± 7.5%; true-positive fractions (TPF-sensitivity) of 85.42 ± 8.1% and 86.1 ± 8.0%; and between the ground truth and the proposed segmentation method, kappa indices (KI) of 84.6% and 85.3% and overlap indices of 74.7% and 75.4%. The segmentation contours were also used to compute the cardiac state identification and radial, longitudinal, and shear strain indices for the CCA wall and plaque between the asymptomatic and symptomatic groups were investigated. The results of this study show that the integrated system investigated in this study can be successfully used for the automated video segmentation of the CCA plaque in ultrasound videos.

Endocardial surface delineation in 3-D transesophageal echocardiography

We describe and compare several methods for recovering endocardial walls from 3-D transesophageal echocardiography (3-D TEE), which can help with diagnostics or providing input into biomechanical models. We employ a segmentation method based on 3-D level sets that maximizes enclosed volume while minimizing surface area and uses a growth inhibition function that includes 3-D gradient magnitude (to locate the endocardial walls) and a thin tissue detector (for the mitral valve leaflets). We also study delineation using a graph cut method that performs automated seeding by leveraging a fast radial symmetry transform to determine a central axis along which the 3-D volume is warped into a cylindrical coordinate space. Finally, a random walker approach is also used for automated delineation. The methods are used to estimate clinically relevant cardiovascular volumetric parameters such as stroke volume and left ventricular ejection fraction. Experiments are performed on clinical data collected from patients undergoing cardiothoracic surgery. Performance evaluation includes comparisons of the automated delineations against expert-defined ground truth using a number of error metrics, as well as errors between automatically computed and expert-derived physiologic parameters.

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.

Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set

An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

Feasibility of Real-Time Three-Dimensional Echocardiography for the Assessment of Distorted Biventricular Systolic Function in Patients with Cor Pulmonale

Background

This study was to investigate the feasibility of real-time 3-dimensional echocardiography (RT3DE) for the analysis of biventricular ejection fractions and volume measurements in patients with cor pulmonale and the correlations of RT3DE results with 64-slice multi-detector cardiac computed tomography (64-MDCT) results.

Methods

This study included a total of 22 patients (59.3 ± 16.6 years of age; 10 males and 12 females) who showed flattening or reverse curvature of the interventricular septum and severe pulmonary hypertension [mean right ventricular (RV) systolic pressure = 66.8 ± 19.7 mmHg] on 2-dimensional transthoracic echocardiography due to cor pulmonale. Biventricular end-diastolic and end-systolic volumes were measured by RT3DE and 64-MDCT. The severity of D-shaped deformation was evaluated by using left ventricular (LV) eccentricity index (ratio of diameters parallel/perpendicular to the interventricular septum on parasternal short axis images of the papillary muscle level).

Results

There were moderate correlations between biventricular volumes measured by RT3DE and 64-MDCT except for LV end-systolic volume (59.8 ± 17.1 vs. 73.2 ± 20.2 mL, r = 0.652, p = 0.001 for LV end-diastolic volume; 30.6 ± 9.1 vs. 30.8 ± 12.5 mL, r = 0.361, p = 0.099 for LV end-systolic volume; 110.1 ± 42.9 vs. 171.1 ± 55.3 mL, r = 0.545, p = 0.009 for RV end-diastolic volume; and 80.9 ± 35.0 vs. 128.7 ± 45.1 mL, r = 0.549, p = 0.005 for RV end-systolic volume respectively).

Conclusion

This study suggests that RT3DE may be a modest method for measuring distorted biventricular end-systolic and end-diastolic volumes in patients with cor pulmonale.

Novel methods for assessment of right heart structure and function in pulmonary hypertension

Long-term increases in pulmonary vascular resistance and pulmonary arterial pressure resulting from structural alterations and abnormal vasoreactivity of the pulmonary vasculature may lead to right ventricular (RV) remodeling. Conventional methods of assessment of RV structure and function do not provide sensitive markers of RV remodeling for prognostic information. Advances in cardiac imaging have provided the capability to obtain quantitative information on the RV structure and function. This article reviews the clinical conditions that result in PH and discusses the novel and emerging methods for the assessment of right heart structure and function in PH in infants and children.

Assessment of right ventricular structure and function in pulmonary hypertension

Right ventricular function plays an important role in determining cardiac symptoms and exercise capacity in chronic heart failure. It is known that right ventricle has complex anatomy and physiology. The purpose of this review paper is to demonstrate the best assessment of the right ventricle with current echocardiography. Echocardiography can assess sufficiently right ventricular structure and function and also suggest prognosis in pulmonary hypertension patients, especially with the use of modern imaging techniques. Finally, the new imaging modality of real time three dimensional echocardiography is interchangeable to cardiac magnetic resonance in reproducibility and accuracy.

A regularization technique for closed contour segmentation in ultrasound images

Segmentation of a target object in the form of a closed curve has many potential applications in medical imaging because it provides quantitative information related to the target objext's size and shape. However, ultrasound image segmentation for boundary delineation of the target object is a very difficult task because of its inherent drawbacks, including uncertainty of the segmentation boundary caused by speckle noise, relatively low SNR, and low contrast. Indeed, in automatic ultrasound image segmentation, conventional techniques with standard regularization often fail to reach the desired segmentation in the form of a simple closed curve because of the weakness of edge detector functions in finding the likely target boundary. In this paper, we propose a new regularization model which has the property of encouraging a closed curve by deliberately controlling the curve smoothness. The new model may be combined with various fitting terms to enhance segmentation results. The key features of the proposed model are demonstrated in detail. Numerical simulations and experiments show that the proposed model enhances the segmentation ability for extracting the target boundary as a closed contour.

Multiview fusion 3-D echocardiography: improving the information and quality of real-time 3-D echocardiography

The advent of real-time 3-D echocardiography (RT3DE) promised dynamic 3-D image acquisition with the potential of a more objective and complete functional analysis. In spite of that, 2-D echocardiography remains the backbone of echocardiography imaging in current clinical practice, with RT3DE mainly used for clinical research. The uptake of RT3DE has been slow because of missing anatomic information, limited field-of-view (FOV) and tedious analysis procedures. This paper presents multiview fusion 3D echocardiography, where multiple images with complementary information are acquired from different probe positions. These multiple images are subsequently aligned and fused together for preserving salient structures in a single, multiview fused image. A novel and simple wavelet-based fusion algorithm is proposed that exploits the low- and high-frequency separation capability of the wavelet analysis. The results obtained show that the proposed multiview fusion considerably improves the contrast (31.1%), contrast-to-noise ratio (46.7%), signal-to-noise ratio (44.7%) and anatomic features (12%) in 3-D echocardiography, and enlarges the FOV (28.2%). This indicates that multiview fusion substantially enhances the image quality and information.

Right ventricular function suffers from reperfusion delay: tissue Doppler study

In this study, impact of reperfusion delay on adverse cardiac events and right ventricular (RV) function in patients with acute right ventricular myocardial infarction (RVMI) was assessed. In 70 patients with RVMI, RV function was assessed by M-mode tricuspid annular plane systolic excursion (TAPSE) and by pulsed wave tissue Doppler echocardiography (TDE). Right ventricular early (E'(T)) and late diastolic (A'(T)), peak systolic tricuspid annular velocity, (S'(T)) and right ventricular myocardial performance index (RVMPI) were measured. Patients were divided into 2 groups according to the time between the onset of symptoms and percutaneous transluminal coronary angioplasty (PTCA)-group 1 (n = 25), < or =3 hours and group 2 (n = 45), > 3 hours. During 30-day follow-up, we assessed adverse cardiac events like the following: death, cardiogenic shock, need for intra-aortic counterpulsation, temporary transvenous pacing support (PCM), presence of ventricular septal defect (VSD), cardiac tamponade, or free wall rupture.

RESULTS

Group 1 had significantly higher values of E'(T), S'(T), and TAPSE than group 2. RVMPI was significantly elevated in group 2. A total of 13 (18.2%) patients had a cardiac event, 5 patients (7.1%) died. We observed an increase of cardiac events in patients with reperfusion delayed more than 3 hours (P = .056).

CONCLUSION

TAPSE-derived and TDE-derived right ventricular systolic and early diastolic velocities were less affected in patients with shorter time from pain onset to opening of the occluded right coronary artery (RCA). Presented initial data show that the tissue Doppler technique may be useful for identification of patients with RV infarction with higher rate of adverse cardiac events including death.

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.

The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking

Real-time 3D echocardiography (RT3DE) promises a more objective and complete cardiac functional analysis by dynamic 3D image acquisition. Despite several efforts towards automation of left ventricle (LV) segmentation and tracking, these remain challenging research problems due to the poor-quality nature of acquired images usually containing missing anatomical information, speckle noise, and limited field-of-view (FOV). Recently, multi-view fusion 3D echocardiography has been introduced as acquiring multiple conventional single-view RT3DE images with small probe movements and fusing them together after alignment. This concept of multi-view fusion helps to improve image quality and anatomical information and extends the FOV. We now take this work further by comparing single-view and multi-view fused images in a systematic study. In order to better illustrate the differences, this work evaluates image quality and information content of single-view and multi-view fused images using image-driven LV endocardial segmentation and tracking. The image-driven methods were utilized to fully exploit image quality and anatomical information present in the image, thus purposely not including any high-level constraints like prior shape or motion knowledge in the analysis approaches. Experiments show that multi-view fused images are better suited for LV segmentation and tracking, while relatively more failures and errors were observed on single-view images.

Developments in echocardiographic techniques for the evaluation of ventricular function in children

Echocardiography is a very important tool for the diagnosis and follow-up of children with congenital and acquired heart disease. One of the challenges that remains in paediatric heart disease is the assessment of systolic and diastolic function in children, as this is influenced by growth, morphology and loading conditions. New echocardiographic techniques, such as tissue Doppler, deformation imaging and three-dimensional echocardiography, have great potential application in this field. They may provide new insights into the influence of growth, morphology and loading on cardiac mechanics, and could become useful clinical tools. In this review, we discuss the potential use and limitations of these new echocardiographic techniques in paediatric and congenital heart disease.

Real-time segmentation by Active Geometric Functions

Recent advances in 4D imaging and real-time imaging provide image data with clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency. In this paper, a novel deformable model framework, Active Geometric Functions (AGF), is introduced to tackle the real-time segmentation problem. As an implicit framework paralleling to level-set, AGF has mathematical advantages in efficiency and computational complexity as well as several flexible feature similar to level-set framework. AGF is demonstrated in two cardiac applications: endocardial segmentation in 4D ultrasound and myocardial segmentation in MRI with super high temporal resolution. In both applications, AGF can perform real-time segmentation in several milliseconds per frame, which was less than the acquisition time per frame. Segmentation results are compared to manual tracing with comparable performance with inter-observer variability. The ability of such real-time segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation.

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.

Right ventricle three-dimensional echography in corrected tetralogy of fallot: accuracy and variability

AIMS

To evaluate right ventricular (RV) volume and ejection fraction (EF) in adult normal subjects and repaired tetralogy of Fallot (ToF) with 3D trans-thoracic echocardiography (3DE) and a semi-automatic border detection algorithm.

METHODS AND RESULTS

Fourteen healthy volunteers and 20 patients with repaired ToF (mean age 31 +/- 14) underwent 3DE and MRI within the same day. Right ventricular end-systolic volume (ESV) and end-diastolic volume (EDV) and EF were measured by two observers using 3DE and compared with MRI measurements. Intra- and interobserver variability of 3DE and agreement between both methods were evaluated using Bland-Altman analysis. Over or underestimation of 3DE in comparison to MRI was assessed using paired t-test. Intra- and interobserver variability of 3DE was excellent with intraclass coefficient of correlation (ICC) ranging from 0.85 to 0.99 and from 0.85 to 0.98, respectively. Three-dimensional echocardiography underestimated ESV and EDV (P < 0.001) but agreement between 3DE and MRI was excellent (ICC = 0.88 and 0.87, respectively). Ejection fraction was 47.7 +/- 7.8 with 3DE and 47.9 +/- 6.7 with MRI, agreement between both methods was good (ICC = 0.72).

CONCLUSION

Three-dimensional echocardiography combined to semi-automated quantification software shows fair agreement with MRI for RV volumes and EF measurement in patients with repaired ToF and adequate intra- and interobserver variability. These results suggest applicability for serial follow-up of patients with right heart congenital disease. However, the accuracy of 3DE echo diminishes with larger RV volumes, in part due to current difficulty to include the entire RV in the imaged sector. Technical progress in transducers beam geometry is likely to address this issue.

Semi-automated quantification of left ventricular volumes and ejection fraction by real-time three-dimensional echocardiography

BACKGROUND

Recent studies have shown that real-time three-dimensional (3D) echocardiography (RT3DE) gives more accurate and reproducible left ventricular (LV) volume and ejection fraction (EF) measurements than traditional two-dimensional methods. A new semi-automated tool (4DLVQ) for volume measurements in RT3DE has been developed. We sought to evaluate the accuracy and repeatability of this method compared to a 3D echo standard.

METHODS

LV end-diastolic volumes (EDV), end-systolic volumes (ESV), and EF measured using 4DLVQ were compared with a commercially available semi-automated analysis tool (TomTec 4D LV-Analysis ver. 2.2) in 35 patients. Repeated measurements were performed to investigate inter- and intra-observer variability.

RESULTS

Average analysis time of the new tool was 141s, significantly shorter than 261s using TomTec (p < 0.001). Bland Altman analysis revealed high agreement of measured EDV, ESV, and EF compared to TomTec (p = NS), with bias and 95% limits of agreement of 2.1 +/- 21 ml, -0.88 +/- 17 ml, and 1.6 +/- 11% for EDV, ESV, and EF respectively. Intra-observer variability of 4DLVQ vs. TomTec was 7.5 +/- 6.2 ml vs. 7.7 +/- 7.3 ml for EDV, 5.5 +/- 5.6 ml vs. 5.0 +/- 5.9 ml for ESV, and 3.0 +/- 2.7% vs. 2.1 +/- 2.0% for EF (p = NS). The inter-observer variability of 4DLVQ vs. TomTec was 9.0 +/- 5.9 ml vs. 17 +/- 6.3 ml for EDV (p < 0.05), 5.0 +/- 3.6 ml vs. 12 +/- 7.7 ml for ESV (p < 0.05), and 2.7 +/- 2.8% vs. 3.0 +/- 2.1% for EF (p = NS).

CONCLUSION

In conclusion, the new analysis tool gives rapid and reproducible measurements of LV volumes and EF, with good agreement compared to another RT3DE volume quantification tool.

3-D speckle tracking for assessment of regional left ventricular function

Speckle tracking in 2-D ultrasound images has become an established tool for assessment of left ventricular function. The recent development of ultrasound systems with capability to acquire real-time full volume data of the left ventricle makes it possible to perform speckle tracking in three dimensions, and thereby track the real motion of the myocardium. This paper presents a method for assessing local strain and rotation from 3-D speckle tracking in apical full-volume datasets. The method has been tested on simulated ultrasound data based on a computer model of the left ventricle, and on patients with myocardial infarction. When applied on simulated ultrasound data, the method showed good agreement with strain and rotation traces calculated from the reference motion, and the method was able to capture segmental differences in the deformation pattern, although the magnitudes of strains were systematically lower than the reference strains. When applied on patients, the method demonstrated reduced strain in the infarcted areas. Bulls-eye plots of regional strains showed good correspondence with wall motion scoring based on 2-D apical images, although the dyskinetic and hypokinetic regions were not apparent in all strain components.

Region-based endocardium tracking on real-time three-dimensional ultrasound

Matrix-phased array transducers for real-time 3-D ultrasound enable fast, noninvasive visualization of cardiac ventricles. Typically, 3-D ultrasound images are semiautomatically segmented to extract the left ventricular endocardial surface at end-diastole and end-systole. Automatic segmentation and propagation of this surface throughout the entire cardiac cycle is a challenging and cumbersome task. If the position of the endocardial surface is provided at one or two time frames during the cardiac cycle, automated tracking of the surface over the remaining time frames could reduce the workload of cardiologists and optimize analysis of 3-D ultrasound data. In this paper, we applied a region-based tracking algorithm to track the endocardial surface between two reference frames that were manually segmented. To evaluate the tracking of the endocardium, the method was applied to 40 open-chest dog datasets with 484 frames in total. Ventricular geometry and volumes derived from region-based endocardial surfaces and manual tracing were quantitatively compared, showing strong correlation between the two approaches. Statistical analysis showed that the errors from tracking were within the range of interobserver variability of manual tracing. Moreover, our algorithm performed well on ischemia datasets, suggesting that the method is robust-to-abnormal wall motion. In conclusion, the proposed optical flow-based surface tracking method is very efficient and accurate, providing dynamic "interpolation" of segmented endocardial surfaces.

Real-time myocardial segmentation using coupled active geometric functions

Myocardial segmentation is essential for quantitative evaluation of cardiac functional images. As imaging techniques advance, 3D and 4D image data have become available. These data can provide clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency and clinical workflow. In this context, an automated real-time myocardial segmentation framework based on coupled Active Geometric Functions was proposed and tested on 414 frames of Phase Train Imaging data, a real-time cardiac MR imaging technique, with an average temporal resolution of 2 ms. The performance of myocardial segmentation was visually and quantitatively validated. Implemented in Matlab(c), the current method takes less than 1.2 ms per cardiac phase, allowing realization of true real-time online segmentation.

Image-driven cardiac left ventricle segmentation for the evaluation of multiview fused real-time 3-dimensional echocardiography images

Real-time 3-dimensional echocardiography (RT3DE) permits the acquisition and visualization of the beating heart in 3D. Despite a number of efforts to automate the left ventricle (LV) delineation from RT3DE images, this remains a challenging problem due to the poor nature of the acquired images usually containing missing anatomical information and high speckle noise. Recently, there have been efforts to improve image quality and anatomical definition by acquiring multiple single-view RT3DE images with small probe movements and fusing them together after alignment. In this work, we evaluate the quality of the multiview fused images using an image-driven semiautomatic LV segmentation method. The segmentation method is based on an edge-driven level set framework, where the edges are extracted using a local-phase inspired feature detector for low-contrast echocardiography boundaries. This totally image-driven segmentation method is applied for the evaluation of end-diastolic (ED) and end-systolic (ES) single-view and multiview fused images. Experiments were conducted on 17 cases and the results show that multiview fused images have better image segmentation quality, but large failures were observed on ED (88.2%) and ES (58.8%) single-view images.

In the spotlight: biomedical imaging

This article resumes on a selected set of topics and collects promising and recent research advances in the field of multimodal temporal data analysis, high-field magnetic resonance spectroscopy, trends in computer-aided diagnosis and advances in cardiac diagnostic imaging. The first section briefly points to promising work on statistical models for tracking, detection, and segmentation in multimodal temporal imagery. Section III gives a brief snapshot of slice selective free induction decay (FID) acquisition for 7 tesla high-field MR imaging. Section IV outlines highlights in comparative validation of computer-aided diagnosis and associated image analysis algorithms spanning a variety of application domains from the heart to the eye. Lastly, Section V describes advances in the analysis of real-time three-dimensional (3-D) echocardiography for computing myocardial strain.

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.

Segmentation of the heart muscle in 3-D pediatric echocardiographic images

This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle.

Normal Values of Right Ventricular Size and Function by Real-time 3-Dimensional Echocardiography: Comparison with Cardiac Magnetic Resonance Imaging

BACKGROUND

Assessment of right ventricular function by 2-dimensional echocardiography (2DECHO) is difficult because of its complex shape. Real-time 3-dimensional echocardiography (RT3DECHO) may be superior.

METHODS

End-diastolic volume, end-systolic volume, stroke volume, and ejection fraction obtained by 2DECHO, RT3DECHO short-axis disk summation (DS), and RT3DECHO apical rotation were compared with cardiac magnetic resonance imaging in 71 healthy individuals.

RESULTS

RT3DECHO DS showed less volume underestimation compared with 2DECHO and RT3DECHO apical rotation. Test-retest variability for RT3DECHO DS end-diastolic volume, end-systolic volume, stroke volume, and ejection fraction were 3.3%, 8.7%, 10%, and 10.3%, respectively. Normal reference ranges of indexed volumes (mean +/- 2SD) for right ventricular end-diastolic volume, end-systolic volume, stroke volume, and ejection fraction were 38.6 to 92.2 mL/m(2), 7.8 to 50.6 mL/m(2), 22.5 to 42.9 mL/m(2), and 38.0% to 65.3%, respectively, for women and 47.0 to 100 mL/m(2), 23.0 to 52.6 mL/m(2), 14.2 to 48.4 mL/m(2), and 29.9% to 58.4%, respectively, for men.

CONCLUSIONS

RT3DECHO DS is superior to RT3DECHO apical rotation and 2DECHO for right ventricular quantification, and performs acceptably when compared with cardiac magnetic resonance imaging in healthy individuals.

Interactive volume rendering of real-time three-dimensional ultrasound images

Real-time, three-dimensional (RT3D) ultrasound allows video frame rate volumetric imaging. The ability to acquire full three-dimensional (3-D) image data in real-time is particularly helpful for applications such as cardiac imaging, which require visualization of complex and dynamic 3-D anatomy. Volume rendering provides a method for intuitive graphical display of the 3-D image data, but capturing the RT3D echo data and performing the necessary processing to generate a volumetric image in real time poses a significant technical challenge. We present a data capture and rendering implementation that uses off-the-shelf components to real-time volume render RT3D ultrasound images. Our approach allowed live, interactive volume rendering of RT3D ultrasound scans.

Segmentation of elastographic images using a coarse-to-fine active contour model

Delineation of radiofrequency-ablation-induced coagulation (thermal lesion) boundaries is an important clinical problem that is not well addressed by conventional imaging modalities. Elastography, which produces images of the local strain after small, externally applied compressions, can be used for visualization of thermal coagulations. This paper presents an automated segmentation approach for thermal coagulations on 3-D elastographic data to obtain both area and volume information rapidly. The approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow, active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the algorithm has been shown to be comparable to manual delineation of coagulations on elastograms by medical physicists (r = 0.99 for volumes of 36 radiofrequency-induced coagulations). Furthermore, the automatic algorithm applied to elastograms yielded results that agreed with manual delineation of coagulations on pathology images (r = 0.96 for the same 36 lesions). This algorithm has also been successfully applied on in vivo elastograms.

Tracking endocardium using optical flow along iso-value curve

In cardiac image analysis, optical flow techniques are widely used to track ventricular borders as well as estimate myocardial motion fields. The optical flow computation is typically performed in Cartesian coordinates, and not constrained from a priori knowledge of normal myocardium deformation patterns. However, for cardiac motion analysis, displacements along specific directions and their derivatives are usually more interesting than 2D or 3D displacement fields themselves. In this context, we propose a general frame work on optical flow estimation along iso-value curves. We applied the proposed framework in a specific application: for endocardium tracking on cine cardiac MRI series. The endocardial surfaces tracked with the proposed algorithm were quantitatively compared with manual tracing at each frame. The proposed method was also compared to the regular Lucas-Kanade optical flow method directly applied to MRI image data in Cartesian coordinates. Quantitative comparison showed a positive improvement in average tracking errors, through the whole cardiac cycle.