Automatic detection of end-diastole and end-systole from echocardiography images using manifold learning

HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos

In the fetal cardiac ultrasound examination, standard cardiac cycle (SCC) recognition is the essential foundation for diagnosing congenital heart disease. Previous studies have mostly focused on the detection of adult CCs, which may not be applicable to the fetus. In clinical practice, localization of SCCs needs to recognize end-systole (ES) and end-diastole (ED) frames accurately, ensuring that every frame in the cycle is a standard view. Most existing methods are not based on the detection of key anatomical structures, which may not recognize irrelevant views and background frames, results containing non-standard frames, or even it does not work in clinical practice. We propose an end-to-end hybrid neural network based on an object detector to detect SCCs from fetal ultrasound videos efficiently, which consists of 3 modules, namely Anatomical Structure Detection (ASD), Cardiac Cycle Localization (CCL), and Standard Plane Recognition (SPR). Specifically, ASD uses an object detector to identify 9 key anatomical structures, 3 cardiac motion phases, and the corresponding confidence scores from fetal ultrasound videos. On this basis, we propose a joint probability method in the CCL to learn the cardiac motion cycle based on the 3 cardiac motion phases. In SPR, to reduce the impact of structure detection errors on the accuracy of the standard plane recognition, we use XGBoost algorithm to learn the relation knowledge of the detected anatomical structures. We evaluate our method on the test fetal ultrasound video datasets and clinical examination cases and achieve remarkable results. This study may pave the way for clinical practices.

AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms

Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in computer-aided coronary artery disease (CAD) diagnosis. However, separating and identifying individual coronary arterial segments is challenging because morphological similarities of different branches on the coronary arterial tree and human-to-human variabilities exist. Inspired by the training procedure of interventional cardiologists for interpreting the structure of coronary arteries, we propose an association graph-based graph matching network (AGMN) for coronary arterial semantic labeling. We first extract the vascular tree from invasive coronary angiography (ICA) and convert it into multiple individual graphs. Then, an association graph is constructed from two individual graphs where each vertex represents the relationship between two arterial segments. Thus, we convert the arterial segment labeling task into a vertex classification task; ultimately, the semantic artery labeling becomes equivalent to identifying the artery-to-artery correspondence on graphs. More specifically, the AGMN extracts the vertex features by the embedding module using the association graph, aggregates the features from adjacent vertices and edges by graph convolution network, and decodes the features to generate the semantic mappings between arteries. By learning the mapping of arterial branches between two individual graphs, the unlabeled arterial segments are classified by the labeled segments to achieve semantic labeling. A dataset containing 263 ICAs was employed to train and validate the proposed model, and a five-fold cross-validation scheme was performed. Our AGMN model achieved an average accuracy of 0.8264, an average precision of 0.8276, an average recall of 0.8264, and an average F1-score of 0.8262, which significantly outperformed existing coronary artery semantic labeling methods. In conclusion, we have developed and validated a new algorithm with high accuracy, interpretability, and robustness for coronary artery semantic labeling on ICAs.

Semi-supervised learning improves the performance of cardiac event detection in echocardiography

Detection of end-diastole (ED) and end-systole (ES) frames in echocardiography video is a critical step for assessment of cardiac function. A recently released large public dataset, i.e., EchoNet-Dynamic, could be used as a benchmark for cardiac event detection. However, only a pair of ED and ES frames are annotated in each echocardiography video and the annotated ED comes before ES in most cases. This means that only a few frames during systole in each video are utilizable for training, which makes it challenging to train a cardiac event detection model using the dataset. Semi-supervised learning (SSL) could alleviate the problems. An architecture combining convolutional neural network (CNN), recurrent neural network (RNN) and fully-connected layers (FC) is adopted. Experimental results indicate that SSL brings at least three benefits: faster convergence rate, performance improvement and more reasonable volume curves. The best mean absolute errors (MAEs) for ED and ES detection are 40.2 ms (2.1 frames) and 32.6 ms (1.7 frames), respectively. In addition, the results show that models trained on apical four-chamber (A4C) view could work well on other standard views, such as other apical views and parasternal short axis (PSAX) views.

Performance evaluation of computer-aided automated master frame selection techniques for fetal echocardiography

PURPOSE

Fetal echocardiography is widely used for the assessment of fetal heart development and detection of congenital heart disease (CHD). Preliminary examination of the fetal heart involves the four-chamber view which indicates the presence of all the four chambers and its structural symmetry. Examination of various cardiac parameters is generally done using the clinically selected diastole frame. This largely depends on the expertise of the sonographer and is prone to intra- and interobservational errors. To overcome this, automated frame selection technique is proposed for the recognition of fetal cardiac chamber from fetal echocardiography.

METHODS

Three techniques have been proposed in this research study to automate the process of determining the frame referred as "Master Frame" that can be used for the measurement of the cardiac parameters. The first method uses frame similarity measures (FSM) for the determination of the master frame from the given cine loop ultrasonic sequences. FSM makes use of similarity measures such as correlation, structural similarity index (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE) to identify the cardiac cycle, and all the frames in one cardiac cycle are superimposed to form the master frame. The final master frame is obtained by considering the average of the master frame obtained using each similarity measure. The second method uses averaging of ± 20% from the midframes (AMF). The third method uses averaging of all the frames (AAF) of the cine loop sequence. Both diastole and master frames have been annotated by the clinical experts, and their ground truths are compared for validation. No segmentation techniques have been used to avoid the variability of the performance of various segmentation techniques. All the proposed schemes were evaluated using six fidelity metrics such as Dice coefficient, Jaccard ratio, Hausdorff distance, structural similarity index, mean absolute error, and Pratt figure of merit.

RESULTS

The three proposed techniques were tested on the frames extracted from 95 ultrasound cine loop sequences between 19 and 32 weeks of gestation. The feasibility of the techniques was determined by the computation of fidelity metrics between the master frame derived and the diastole frame chosen by the clinical experts. The FSM-based identified master frame found to closely match with manually chosen diastole frame and also ensures statistically significant. The method also detects automatically the cardiac cycle. The resultant master frame obtained through AMF though found to be identical to that of the diastole frame, the size of the chambers found to be reduced that can lead to inaccurate chamber measurement. The master frame obtained through AAF was not found to be identical to that of clinical diastole frame.

CONCLUSION

It can be concluded that the frame similarity measure (FSM)-based master frame can be introduced in the clinical routine for segmentation followed by cardiac chamber measurements. Such automated master frame selection also overcomes the manual intervention of earlier reported techniques in the literature. The fidelity metrics assessment further confirms the suitability of proposed master frame for automated fetal chamber recognition.

Automatic identification of end-diastolic and end-systolic cardiac frames from invasive coronary angiography videos

BACKGROUND

Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible.

OBJECITVE

In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases.

METHOD

A detection algorithm is first used to detect the key points (i.e. landmarks) of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points. The ED and ES frames are identified based on all these trajectories. Our method was tested with 62 ICA videos from two separate medical centers.

RESULTS

Comparing consensus interpretations by two human expert readers, excellent agreement was achieved by the proposed algorithm: the agreement rates within a one-frame range were 92.99% and 92.73% for the automatic identification of the ED and ES image frames, respectively.

CONCLUSION

The proposed automated method showed great potential for being an integral part of automated ICA image analysis.

Master Frame Extraction of Fetal Cardiac Images Using B Mode Ultrasound Images

Fetal Echocardiography is used for monitoring the fetal heart and for detection of Congenital Heart Disease (CHD). It is well known that fetal cardiac four chamber view has been widely used for preliminary examination for the detection of CHD. The end diastole frame is generally used for the analysis of the fetal cardiac chambers which is manually picked by the clinician during examination/screening. This method is subjected to intra and inter observer errors and also time consuming. The proposed study aims to automate this process by determining the frame, referred to as the Master frame from the cine loop sequences that can be used for the analysis of the fetal heart chambers instead of the clinically chosen diastole frame. The proposed framework determines the correlation between the reference (first) frame with the successive frames to identify one cardiac cycle. Then the Master frame is formed by superimposing all the frames belonging to one cardiac cycle. The master frame is then compared with the clinically chosen diastole frame in terms of fidelity metrics such as Dice coefficient, Hausdorff distance, mean square error and structural similarity index. The average value of the fidelity metrics considering the dataset used for this study 0.73 for Dice, 13.94 for Hausdorff distance, 0.99 for Structural Similarity Index and 0.035 for mean square error confirms the suitability of the proposed master frame extraction thereby avoiding manual intervention by the clinician. .

Stable Automatic Envelope Estimation for Noisy Doppler Ultrasound

Doppler ultrasound technology is widespread in clinical applications and is principally used for blood flow measurements in the heart, arteries, and veins. A commonly extracted parameter is the maximum velocity envelope. However, current methods of extracting it cannot produce stable envelopes in high noise conditions. This can limit clinical and research applications using the technology. In this article, a new method of automatic envelope estimation is presented. The method can handle challenging signals with high levels of noise and variable envelope shapes. Envelopes are extracted from a Doppler spectrogram image generated directly from the Doppler audio signal, making it less device-dependent than existing image-processing methods. The method's performance is assessed using simulated pulsatile flow, a flow phantom, and in vivo ascending aortic flow measurements and is compared with three state-of-the-art methods. The proposed method is the most accurate in noisy conditions, achieving, on average, for phantom data with signal-to-noise ratios (SNRs) below 10 dB, bias and standard deviation of 0.7% and 3.3% lower than the next-best performing method. In addition, a new method for beat segmentation is proposed. When combined, the two proposed methods exhibited the best performance using in vivo data, producing the least number of incorrectly segmented beats and 8.2% more correctly segmented beats than the next best performing method. The ability of the proposed methods to reliably extract timing indices for cardiac cycles across a range of signal quality is of particular significance for research and monitoring applications.

Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning

Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D echocardiography are associated with a high uncertainty not only due to interobserver variability of the manual measurement, but also due to ultrasound acquisition errors such as apical foreshortening. In this work, a real-time and fully automated EF measurement and foreshortening detection method is proposed. The method uses several deep learning components, such as view classification, cardiac cycle timing, segmentation and landmark extraction, to measure the amount of foreshortening, LV volume, and EF. A data set of 500 patients from an outpatient clinic was used to train the deep neural networks, while a separate data set of 100 patients from another clinic was used for evaluation, where LV volume and EF were measured by an expert using clinical protocols and software. A quantitative analysis using 3-D ultrasound showed that EF is considerably affected by apical foreshortening, and that the proposed method can detect and quantify the amount of apical foreshortening. The bias and standard deviation of the automatic EF measurements were -3.6 ± 8.1%, while the mean absolute difference was measured at 7.2% which are all within the interobserver variability and comparable with related studies. The proposed real-time pipeline allows for a continuous acquisition and measurement workflow without user interaction, and has the potential to significantly reduce the time spent on the analysis and measurement error due to foreshortening, while providing quantitative volume measurements in the everyday echo lab.

Deep neural networks for ECG-free cardiac phase and end-diastolic frame detection on coronary angiographies

Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional assessment of CAD. This task is generally performed via manual frame selection or semi-automated selection based on simultaneously acquired ECG signals - thus introducing the requirement of simultaneous ECG recordings. In this paper, we evaluate the performance of a purely image based workflow relying on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiographies. A first deep neural network (DNN), trained to detect coronary arteries, is employed to preselect a subset of frames in which coronary arteries are well visible. A second DNN predicts cardiac phase labels for each frame. Only in the training and evaluation phases for the second DNN, ECG signals are used to provide ground truth labels for each angiographic frame. The networks were trained on 56,655 coronary angiographies from 6820 patients and evaluated on 20,780 coronary angiographies from 6261 patients. No exclusion criteria related to patient state (stable or acute CAD), previous interventions (PCI or CABG), or pathology were formulated. Cardiac phase detection had an accuracy of 98.8 %, a sensitivity of 99.3 % and a specificity of 97.6 % on the evaluation set. EDF prediction had a precision of 98.4 % and a recall of 97.9 %. Several sub-group analyses were performed, indicating that the cardiac phase detection performance is largely independent from acquisition angles, the heart rate of the patient, and the angiographic view (LCA / RCA). The average execution time of cardiac phase detection for one angiographic series was on average less than five seconds on a standard workstation. We conclude that the proposed image based workflow potentially obviates the need for manual frame selection and ECG acquisition, representing a relevant step towards automated CAD assessment.

Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography

BACKGROUND

Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain.

METHODS AND RESULTS

We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms (P<.0001). Put another way, the algorithm-identified time point was an outlier in 122/564 heartbeats (21.6%), whereas the average human operator was an outlier in 254/564 heartbeats (45%).

CONCLUSION

An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame.

Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms

Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).

Semiautomatic Evaluation of Tricuspid Annular Plane Systolic Excursion from Two Dimensional Echocardiographic Images

BACKGROUND

Tricuspid annular plane systolic excursion (TAPSE) has emerged as a reliable marker of right ventricular (RV) systolic function. Recently, TAPSE derived using 2D images (2D-TAPSE) was shown to correlate with M-mode TAPSE (MM-TAPSE). We have developed a novel technique for semiautomatic evaluation of TAPSE (SA-TAPSE). The purpose of this study was to determine the accuracy of this novel technique and validate it on normal hearts and pulmonary hypertension (PH).

METHODS

A total of 110 patients (56 with normal heart and 54 with PH) were retrospectively identified for analysis. The semiautomatic algorithm tracked the lateral tricuspid valve hinge point (TVHP) and the apex in the apical 4-chamber view. SA-TAPSE was calculated as displacement of the TVHP in end-diastole (ED) and end-systole (ES). The same points were manually identified to derive 2D-TAPSE.

RESULTS

The system was able to accurately identify ED and ES in 304/330 heartbeats within three cardiac frames. The automatically identified TVHP points were within 1.2 ± 0.7 mm from the manually identified points. Intra-class correlation between SA-TAPSE and 2D-TAPSE was 0.96 (95% CI 0.93-0.98) for normal hearts and 0.92 (95% CI 0.87-0.96) for those with PH. Bland-Altman analysis showed a strong agreement between SA-TAPSE and 2D-TAPSE for normal hearts and those with PH.

CONCLUSION

Using the novel custom-made software, SA-TAPSE could be measured in majority of our patients and was accurate when applied to normal hearts and those with PH. Future work will focus on fully automating the system for a rapid retrospective analysis of TAPSE.

Echocardiography without electrocardiogram using nonlinear dimensionality reduction methods

PURPOSE

The aim of this study is to evaluate the efficiency of a new automatic image processing technique, based on nonlinear dimensionality reduction (NLDR) to separate a cardiac cycle and also detect end-diastole (ED) (cardiac cycle start) and end-systole (ES) frames on an echocardiography system without using ECG.

METHODS

Isometric feature mapping (Isomap) and locally linear embeddings (LLE) are the most popular NLDR algorithms. First, Isomap algorithm is applied on recorded echocardiography images. By this approach, the nonlinear embedded information in sequential images is represented in a two-dimensional manifold and each image is characterized by a symbol on the constructed manifold. Cyclicity analysis of the resultant manifold, which is derived from the cyclic nature of the heart motion, is used to perform cardiac cycle length estimation. Then, LLE algorithm is applied on extracted left ventricle (LV) echocardiography images of one cardiac cycle. Finally, the relationship between consecutive symbols of the resultant manifold by the LLE algorithm, which is based on LV volume changes, is used to estimate ED (cycle start) and ES frames. The proposed algorithms are quantitatively compared to those obtained by a highly experienced echocardiographer from ECG as a reference in 20 healthy volunteers and 12 subjects with pathology.

RESULTS

Mean difference in cardiac cycle length, ED, and ES frame estimation between our method and ECG detection by the experienced echocardiographer is approximately 7, 17, and 17 ms (0.4, 1, and 1 frame), respectively.

CONCLUSION

The proposed image-based method, based on NLDR, can be used as a useful tool for estimation of cardiac cycle length, ED and ES frames in echocardiography systems, with good agreement to ECG assessment by an experienced echocardiographer in routine clinical evaluation.

Automatic computation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reduction

Curve of left ventricular (LV) volume changes throughout the cardiac cycle is a fundamental parameter for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is often performed manually which is tedious and time consuming and suffers from significant interobserver and intraobserver variability. This paper introduces a new automatic method, based on nonlinear dimensionality reduction (NLDR) for extracting the curve of the LV volume changes over a cardiac cycle from two-dimensional (2-D) echocardiography images. Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this study, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on 2-D echocardiography images of one cycle of heart. Using this approach, the nonlinear information of these images is embedded in a 2-D manifold and each image is characterized by a symbol on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes and allows extracting the curve of the LV volume changes automatically. Our method in comparison to the traditional segmentation algorithms does not need any LV myocardial segmentation and tracking, particularly difficult in the echocardiography images. Moreover, a large data set under various diseases for training is not required. The results obtained by our method are quantitatively evaluated to those obtained manually by the highly experienced echocardiographer on ten healthy volunteers and six patients which depict the usefulness of the presented method.

Real-time monitoring of high-intensity focused ultrasound thermal therapy using the manifold learning method

High-intensity focused ultrasound (HIFU) induces thermal lesions by increasing the tissue temperature in a tight focal region. The main ultrasound imaging techniques currently used to monitor HIFU treatment are standard pulse-echo B-mode ultrasound imaging, ultrasound temperature estimation and elastography-based methods. The present study was carried out on ex vivo animal tissue samples, in which backscattered radiofrequency (RF) signals were acquired in real time at time instances before, during and after HIFU treatment. The manifold learning algorithm, a non-linear dimensionality reduction method, was applied to RF signals whichconstruct B-mode images to detect the HIFU-induced changes among the image frames obtained during HIFU treatment. In this approach, the embedded non-linear information in the region of interest of sequential images is represented in a 2-D manifold with the Isomap algorithm, and each image is depicted as a point on the reconstructed manifold. Four distinct regions are chosen in the manifold corresponding to the four phases of HIFU treatment (before HIFU treatment, during HIFU treatment, immediately after HIFU treatment and 10-min after HIFU treatment). It was found that disorganization of the points is achieved by increasing the acoustic power, and if the thermal lesion has been formed, the regions of points related to pre- and post-HIFU significantly differ. Moreover, the manifold embedding was repeated on 2-D moving windows in RF data envelopes related to pre- and post-HIFU exposure data frames. It was concluded that if mean values of the points related to pre- and post-exposure frames in the reconstructed manifold are estimated, and if the Euclidean distance between these two mean values is calculated and the sliding window is moved and this procedure is repeated for the whole image, a new image based on the Euclidean distance can be formed in which the HIFU thermal lesion is detectable.

Automated aortic Doppler flow tracing for reproducible research and clinical measurements

In clinical practice, echocardiographers are often unkeen to make the significant time investment to make additional multiple measurements of Doppler velocity. Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyze more beats, we present the description of an application system for automated aortic Doppler envelope quantification, compatible with a range of hardware platforms. It analyses long Doppler strips, spanning many heartbeats, and does not require electrocardiogram to separate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence (R(2) = 0.94) and good Bland-Altman agreement (SD = 1.39 cm) with the reference consensus expert values, and indeed performed as well as the individual experts ( R(2) = 0.90 to 0.96, SD = 1.05 to 1.53 cm). The same performance was observed for peak-velocities; ( R(2) = 0.98, SD = 3.07 cm/s) and ( R(2) = 0.93 to 0.98, SD = 2.96 to 5.18 cm/s). This automated technology allows > 10 times as many beats to be analyzed compared to the conventional manual approach. This would make clinical and research protocols more precise for the same operator effort.

Automatic assessment of regional and global wall motion abnormalities in echocardiography images by nonlinear dimensionality reduction

PURPOSE

Identification and assessment of left ventricular (LV) global and regional wall motion (RWM) abnormalities are essential for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is performed visually which is highly dependent on the training and experience of echocardiographers and thus is prone to considerable interobserver and intraobserver variability. This paper presents a new automatic method, based on nonlinear dimensionality reduction (NLDR) for global wall motion evaluation and also detection and classification of RWM abnormalities of LV wall in a three-point scale as follows: (1) normokinesia, (2) hypokinesia, and (3) akinesia.

METHODS

Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this paper, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on two-dimensional (2D) echocardiography images of one cycle of heart. By this approach, nonlinear information in these images is embedded in a 2D manifold and each image is characterized by a point on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes. Then, a new global and regional quantitative index from the resultant manifold is proposed for global wall motion estimation and also classification of RWM of LV wall in a three-point scale. Obtained results by our method are quantitatively evaluated to those obtained visually by two experienced echocardiographers as the reference (gold standard) on 10 healthy volunteers and 14 patients.

RESULTS

Linear regression analysis between the proposed global quantitative index and the global wall motion score index and also with LV ejection fraction obtained by reference experienced echocardiographers resulted in the correlation coefficients of 0.85 and 0.90, respectively. Comparison between the proposed automatic RWM scoring and the reference visual scoring resulted in an absolute agreement of 82% and a relative agreement of 97%.

CONCLUSIONS

The proposed diagnostic method can be used as a useful tool as well as a reference visual assessment by experienced echocardiographers for global wall motion estimation and also classification of RWM abnormalities of LV wall in a three-point scale in clinical evaluations.

Measuring Left Ventricular Volumes in Two-Dimensional Echocardiography Image Sequence Using Level-set Method for Automatic Detection of End-Diastole and End-systole Frames

BACKGROUND

Identifying End-Diastole (ED) and End-Systole (ES) frames is highly important in the process of evaluating cardiac function and measuring global parameters accurately, such as Ejection Fraction (EF), Cardiac Output (CO) and Stroke Volume.

OBJECTIVES

The current study aimed to develop a new method based on measuring volume changes in Left Ventricle (LV) during cardiac cycle.

MATERIAL AND METHODS

For this purpose, the Level Set method was used both in detecting endocardium border and quantifying cardiac function of all frames.

RESULTS

Demonstrating LV volumes displays ED and ES frames and the volumes used in calculating the required parameters.

CONCLUSIONS

Since ES and ED frames exist in iso-volumic phases of the cardiac cycle with minimum and maximum values of LV volume signals, such peaks can be utilized in finding related frames.

Left ventricle wall motion quantification from echocardiographic images by non-rigid image registration

PURPOSE

The aim of this study is to evaluate the efficiency of applying a new non-rigid image registration method on two-dimensional echocardiographic images for computing the left ventricle (LV) myocardial motion field over a cardiac cycle.

METHODS

The key feature of our method is to register all images in the sequence to a reference image (end-diastole image) using a hierarchical transformation model, which is a combination of an affine transformation for modeling the global LV motion and a free-form deformation (FFD) transformation based on B-splines for modeling the local LV deformation. Registration is done by minimizing a cost function associated with the image similarity based on a global pixel-based matching and the smoothness of transformation. The algorithm uses a fast and robust optimization strategy using a multiresolution approach for the estimation of parameters of the deformation model. The proposed algorithm is evaluated for calculating the displacement curves of two expert-identified anatomical landmarks in apical views of the LV for 10 healthy volunteers and 14 subjects with pathology. The proposed algorithm is also evaluated for classifying the regional LV wall motion abnormality using the calculation of the strain value at the end of systole in 288 segments as scored by two consensual experienced echocardiographers in a three-point scale: 1: normokinesia, 2: hypokinesia, and 3: akinesia. Moreover, we compared the results of the proposed registration algorithm to those previously obtained using the other image registration methods.

RESULTS

Regarding to the reference two experienced echocardiographers, the results demonstrate the proposed algorithm more accurately estimates the displacement curve of the two anatomical landmarks in apical views than the other registration methods in all data set. Moreover, the p values of the t test for the strain value of each segment at the end of systole measured by the proposed algorithm show higher differences than the other registration method. These differences are between each pair of scores in all segments and in three segments of septum independently.

CONCLUSIONS

The clinical results show that the proposed algorithm can improve both the calculation of the displacement curve of every point of LV during a cardiac cycle and the classification of regional LV wall motion abnormality. Therefore, this diagnostic system can be used as a useful tool for clinical evaluation of the regional LV function.