Ultrasound plane wave imaging is widely used in many applications thanks to its capability in reaching high frame rates. However, the amount of data acquisition and storage in a period of time can become a bottleneck in ultrasound system design for thousands frames per second. In our previous study, we proposed a low-rank and joint-sparse model to reduce the amount of sampled channel data of focused beam imaging by considering all the received data as a 2D matrix. However, for a single plane wave transmission, the number of channels is limited and the low-rank property of the received data matrix is no longer achieved. In this study, a L0-norm based Hankel structured low-rank and sparse model is proposed to reduce the channel data. An optimization algorithm, based on the alternating direction method of multipliers (ADMM), is proposed to efficiently solve the resulting optimization problem. The performance of the proposed approach was evaluated using the data published in Plane Wave Imaging Challenge in Medical Ultrasound (PICMUS) in 2016. Results on channel and plane wave data show that the proposed method is better adapted to the ultrasound channel signal and can recover the image with fewer samples than the conventional CS method.

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.

Over the years, medical image tracking has gained considerable attention from both medical and research communities due to its widespread utility in a multitude of clinical applications, from functional assessment during diagnosis and therapy planning to structure tracking or image fusion during image-guided interventions. Despite the ever-increasing number of image tracking methods available, most still consist of independent implementations with specific target applications, lacking the versatility to deal with distinct end-goals without the need for methodological tailoring and/or exhaustive tuning of numerous parameters. With this in mind, we have developed the medical image tracking toolbox (MITT)-a software package designed to ease customization of image tracking solutions in the medical field. While its workflow principles make it suitable to work with 2-D or 3-D image sequences, its modules offer versatility to set up computationally efficient tracking solutions, even for users with limited programming skills. MITT is implemented in both C/C++ and MATLAB, including several variants of an object-based image tracking algorithm and allowing to track multiple types of objects (i.e., contours, multi-contours, surfaces, and multi-surfaces) with several customization features. In this paper, the toolbox is presented, its features discussed, and illustrative examples of its usage in the cardiology field provided, demonstrating its versatility, simplicity, and time efficiency.

The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3-D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3-D synthetic cardiac US and MR (both cine and tagging) image sequences from the same virtual patient. A state-of-the-art electromechanical (E/M) model was exploited for simulating groundtruth cardiac motion fields ranging from healthy to various pathological cases, including both ventricular dyssynchrony and myocardial ischemia. The E/M groundtruth along with template MR/US images and physical simulators were combined in a unified framework for generating synthetic data. We efficiently merged several warping strategies to keep the full control of myocardial deformations while preserving realistic image texture. In total, we generated 18 virtual patients, each with synthetic 3-D US, cine MR, and tagged MR sequences. The simulated images were evaluated both qualitatively by showing realistic textures and quantitatively by observing myocardial intensity distributions similar to real data. In particular, the US simulation showed a smoother myocardium/background interface than the state-of-the-art. We also assessed the mechanical properties. The pathological subjects were discriminated from the healthy ones by both global indexes (ejection fraction and the global circumferential strain) and regional strain curves. The synthetic database is comprehensive in terms of both pathology and modality, and has a level of realism sufficient for validation purposes. All the 90 sequences are made publicly available to the research community via an open-access database.

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81±0.59 and 1.98±0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.

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.

Myocardial deformation imaging can provide valuable insights in myocardial mechanics and help in the diagnosis, prognosis and follow-up of cardiac diseases. However, extracting these indices in 3D is challenging due to the limitations in spatial and temporal resolution of the current volumetric ultrasound systems. For this purpose, we developed an anatomical free-form deformation image registration framework which is locally adapted to the anatomy of the heart. In this work we explored whether incorporating a myocardial volume conservation regularizer would improve strain estimates. We evaluated our technique on in silico echo sequences featuring realistic speckle textures and showed the volume conservation regularizer to be beneficial in reducing strain errors further when used in combination with a smoothness penalty. This combination led to more physiological boundary conditions. It also made distinguishing ischemic from normal segments easier in clinical images.

Although modern ultrasound acquisition systems allow recording of 3D echocardiographic images, tracking anatomical structures from them is still challenging. In addition, since these images are typically created from information obtained across several cardiac cycles, it is not yet possible to acquire high-quality 3D images from patients presenting varying heart rhythms. In this paper, we propose a method to estimate the motion field from multi-plane echocardiographic images of the left ventricle, which are acquired simultaneously during a single cardiac cycle. The method integrates tri-plane B-mode and tissue Doppler images acquired at different rotation angles around the long axis of the left ventricle. It uses a diffeomorphic continuous spatio-temporal transformation model with a spherical data representation for a better interpolation in the circumferential direction. This framework allows exploiting the spatial relation among the acquired planes. In addition, higher temporal resolution of the transformation in the beam direction is achieved by uncoupling the estimation of the different components of the velocity field. The method was validated using a realistic synthetic dataset including healthy and ischemic cases, obtaining errors of 0.14 ± 0.09 mm for displacements, 0.96 ± 1.03% for longitudinal strain and 3.94 ± 4.38% for radial strain estimation. In addition, the method was also demonstrated on a healthy volunteer and two patients with ischemia.

A novel semi-automatic algorithm for aortic valve (AV) wall segmentation is presented for 3D transesophageal echocardiography (TEE) datasets. The proposed methodology uses a 3D cylindrical formulation of the B-spline Explicit Active Surfaces (BEAS) framework in a dual-stage energy evolution process, comprising a threshold-based and a localized region-based stage. Hereto, intensity and shape-based features are combined to accurately delineate the AV wall from the ascending aorta (AA) to the left ventricular outflow tract (LVOT). Shape-prior information is included using a profile-based statistical shape model (SSM), and embedded in BEAS through two novel regularization terms: one confining the segmented AV profiles to shapes seen in the SSM (hard regularization) and another penalizing according to the profile's degree of likelihood (soft regularization). The proposed energy functional takes thus advantage of the intensity data in regions with strong image content, while complementing it with shape knowledge in regions with nearly absent image data. The proposed algorithm has been validated in 20 3D-TEE datasets with both stenotic and non-stenotic valves. It was shown to be accurate, robust and computationally efficient, taking less than 1 second to segment the AV wall from the AA to the LVOT with an average accuracy of 0.78 mm. Semi-automatically extracted measurements at four relevant anatomical levels (LVOT, aortic annulus, sinuses of Valsalva and sinotubular junction) showed an excellent agreement with experts' ones, with a higher reproducibility than manually-extracted measures.

Color tissue Doppler imaging (TDI) is a well-established methodology to assess local myocardial motion/deformation. Typically, a frame rate of ∼ 200 Hz can be achieved by imaging a narrow sector (∼ 30°, covering one cardiac wall) at moderate line density, using a dedicated pulse sequence and multi-line acquisition. However, a wide angle field-of-view is required in some clinical applications to image the whole left ventricle, which implies a drop in temporal resolution. Hereto, the aim of this study was to propose a novel imaging sequence using a multi-line transmit (MLT) beamforming approach to achieve high frame rate color TDI while preserving a wide field-of-view (i.e., 90° sector). To this end, a color MLT-TDI sequence achieving a frame rate of 208 Hz with a 90°-sector was implemented on an ultrasound experimental scanner interleaved with a conventional color TDI sequence achieving the same frame rate but only with a 22.5°-sector. Using this setup, the septal wall of 9 healthy volunteers was imaged and the corresponding velocity was extracted. The M-mode velocity images and the velocity profiles obtained from the MLT-TDI images presented physiologic patterns, very similar to those from conventional TDI. Moreover, for the peak systolic/diastolic velocities, good agreement and strong correlation between MLT-TDI and conventional TDI were found. The results thus demonstrate the feasibility of the novel MLT based TDI methodology to achieve high frame rate color TDI without compromising the field-of-view. This may open the opportunity to simultaneously assess regional myocardial function of the whole left ventricle at high temporal resolution.

Quantification of cardiac deformation and strain with 3D ultrasound takes considerable research efforts. Nevertheless, a widespread use of these techniques in clinical practice is still held back due to the lack of a solid verification process to quantify and compare performance. In this context, the use of fully synthetic sequences has become an established tool for initial in silico evaluation. Nevertheless, the realism of existing simulation techniques is still too limited to represent reliable benchmarking data. Moreover, the fact that different centers typically make use of in-house developed simulation pipelines makes a fair comparison difficult. In this context, this paper introduces a novel pipeline for the generation of synthetic 3D cardiac ultrasound image sequences. State-of-the art solutions in the fields of electromechanical modeling and ultrasound simulation are combined within an original framework that exploits a real ultrasound recording to learn and simulate realistic speckle textures. The simulated images show typical artifacts that make motion tracking in ultrasound challenging. The ground-truth displacement field is available voxelwise and is fully controlled by the electromechanical model. By progressively modifying mechanical and ultrasound parameters, the sensitivity of 3D strain algorithms to pathology and image properties can be evaluated. The proposed pipeline is used to generate an initial library of 8 sequences including healthy and pathological cases, which is made freely accessible to the research community via our project web-page.

Tracking of moving objects in an image sequence is an important task in many application (e.g. medical imaging, robotics). However, this task is usually difficult due to inherent problems that could happen in sequences (i.e. possible occlusion of the object, large interframe motion). In this paper, we describe a new approach to integrate a priori motion information into a level-set-based active contour approach. Specifically, we introduce a new constraint that enforces the conservation of the levels of the implicit function along the image sequence. This constraint is formulated as a motion prior energy and is used in a tracking algorithm. The method is validated quantitatively on a clinical application based on 10 echocar-diographic and 5 cine-MRI sequences (≈ 700 images).

We propose a technique for myocardial motion estimation based on image registration using both B-mode echocardiographic images and tissue Doppler sequences acquired interleaved. The velocity field is modeled continuously using B-splines and the spatiotemporal transform is constrained to be diffeomorphic. Images before scan conversion are used to improve the accuracy of the estimation. The similarity measure includes a model of the speckle pattern distribution of B-mode images. It also penalizes the disagreement between tissue Doppler velocities and the estimated velocity field. Registration accuracy is evaluated and compared to other alternatives using a realistic synthetic dataset, obtaining mean displacement errors of about 1 mm. Finally, the method is demonstrated on data acquired from six volunteers, both at rest and during exercise. Robustness is tested against low image quality and fast heart rates during exercise. Results show that our method provides a robust motion estimate in these situations.

High frame rate (HFR) echocardiography may be of benefit for functional analysis of the heart. In current clinical equipment, HFR is obtained using multi-line acquisition (MLA) which typically requires broadening of transmit beams. As this may result in a significant degradation of spatial resolution and signal-to-noise ratio (SNR), the capacity of MLA to obtain high quality HFR images remains limited. As an alternative, we have demonstrated by computer simulation that simultaneously transmitting multiple focused beams into different directions [multi-line transmit (MLT)], can increase the frame rate without significantly compromising the spatial resolution or SNR. This study aimed to experimentally verify these theoretical predictions both in vitro and in vivo to demonstrate, for the first time, that cardiac MLT imaging is feasible. Hereto, the ultrasound advanced open platform, equipped with a 2.0 MHz phased array, was programmed to interleave MLT and conventional single line transmit (SLT) beam forming. Using these two beam forming methods, images of phantoms and healthy volunteers were acquired and investigated both qualitatively and quantitatively. The results confirmed our simulations that image quality of a 4MLT imaging system with a Tukey apodization scheme is very competitive to that of SLT while providing a 4 times higher frame rate. It is also demonstrated that MLT can be combined with MLA to provide images at 12- to 16-fold frame rate (about 340-450 Hz) without significantly compromising spatial resolution and SNR. This is thus the first study to demonstrate that this new ultrasound imaging paradigm is viable which could have significant impact on future cardiac ultrasound systems.

This paper evaluates five 3D ultrasound tracking algorithms regarding their ability to quantify abnormal deformation in timing or amplitude. A synthetic database of B-mode image sequences modeling healthy, ischemic and dyssynchrony cases was generated for that purpose. This database is made publicly available to the community. It combines recent advances in electromechanical and ultrasound modeling. For modeling heart mechanics, the Bestel-Clement-Sorine electromechanical model was applied to a realistic geometry. For ultrasound modeling, we applied a fast simulation technique to produce realistic images on a set of scatterers moving according to the electromechanical simulation result. Tracking and strain accuracies were computed and compared for all evaluated algorithms. For tracking, all methods were estimating myocardial displacements with an error below 1 mm on the ischemic sequences. The introduction of a dilated geometry was found to have a significant impact on accuracy. Regarding strain, all methods were able to recover timing differences between segments, as well as low strain values. On all cases, radial strain was found to have a low accuracy in comparison to longitudinal and circumferential components.

Despite the availability of multiple solutions for assessing myocardial strain by ultrasound, little is currently known about the relative performance of the different methods. In this study, we sought to contrast two strain estimation techniques directly (speckle tracking and elastic registration) in an in vivo setting by comparing both to a gold standard reference measurement. In five open-chest sheep instrumented with ultrasonic microcrystals, 2-D images were acquired with a GE Vivid7 ultrasound system. Radial (ε(RR)), longitudinal (ε(LL)), and circumferential strain (ε(CC)) were estimated during four inotropic stages: at rest, during esmolol and dobutamine infusion, and during acute ischemia. The correlation of the end-systolic strain values of a well-validated speckle tracking approach and an elastic registration method against sonomicrometry were comparable for ε(LL) ( r=0.70 versus r=0.61, respectively; p=0.32) and ε(CC) ( r=0.73 versus r=0.80 respectively; p=0.31). However, the elastic registration method performed considerably better for ε(RR) ( r=0.64 versus r=0.85 respectively; p=0.09). Moreover, the bias and limits of agreement with respect to the reference strain estimates were statistically significantly smaller in this direction . This could be related to regularization which is imposed during the motion estimation process as opposed to an a posteriori regularization step in the speckle tracking method. Whether one method outperforms the other in detecting dysfunctional regions remains the topic of future research.

A new formulation of active contours based on explicit functions has been recently suggested. This novel framework allows real-time 3-D segmentation since it reduces the dimensionality of the segmentation problem. In this paper, we propose a B-spline formulation of this approach, which further improves the computational efficiency of the algorithm. We also show that this framework allows evolving the active contour using local region-based terms, thereby overcoming the limitations of the original method while preserving computational speed. The feasibility of real-time 3-D segmentation is demonstrated using simulated and medical data such as liver computer tomography and cardiac ultrasound images.

We introduce in this paper a formulation of region-based active contours using explicit surfaces. By formally relating the explicit formulation to the usual implicit formulation, we show that it allows to take advantage of the local or global region-based functionals designed in the level-set framework, while providing a fast algorithm yielding close to real-time segmentation of 3D objects. We compare the segmentation performance of the proposed method with the fast level-set based method of Shi [8]. In similar conditions, the proposed method is 26x to 46x faster. Furthermore, we show that it can handle challenging, inhomogeneous data, while keeping a low computational burden.

Current ultrasound methods for measuring myocardial strain are often limited to measurements in one or two dimensions. Cardiac motion and deformation however are truly 3-D. With the introduction of matrix transducer technology, 3-D ultrasound imaging of the heart has become feasible but suffers from low temporal and spatial resolution, making 3-D strain estimation challenging. In this paper, it is shown that automatic intensity-based spatio-temporal elastic registration of currently available 3-D volumetric ultrasound data sets can be used to measure the full 3-D strain tensor. The method was validated using simulated 3-D ultrasound data sets of the left ventricle (LV). Three types of data sets were simulated: a normal and symmetric LV with different heart rates, a more realistic asymmetric normal LV and an infarcted LV. The absolute error in the estimated displacement was between 0.47 +/-0.23 and 1.00 +/-0.59 mm, depending on heart rate and amount of background noise. The absolute error on the estimated strain was 9%-21% for the radial strain and 1%-4% for the longitudinal and circumferential strains. No large differences were found between the different types of data sets. The shape of the strain curves was estimated properly and the position of the infarcts could be identified correctly. Preliminary results on clinical data taken in vivo from three healthy volunteers and one patient with an apical aneurism confirmed these findings in a qualitative manner as the strain curves obtained with the proposed method have an amplitude and shape similar to what could be expected.

Ultrasonic imaging is the noninvasive clinical imaging modality of choice for diagnosing heart disease. At present, two-dimensional ultrasonic grayscale images provide a relatively cheap, fast, bedside method to study the morphology of the heart. Several methods have been proposed to assess myocardial function. These have been based on either grayscale or motion (velocity) information measured in real-time. However, the quantitative assessment of regional myocardial function remains an important goal in clinical cardiology. To do this, ultrasonic strain and strain-rate imaging have been introduced. In the clinical setting, these techniques currently only allow one component of the true three-dimensional deformation to be measured. Clinical, multidimensional strain (rate) information can currently thus only be obtained by combining data acquired using different transducer positions. Nevertheless, given the appropriate postprocessing, the clinical value of these techniques has already been shown. Moreover, multidimensional strain and strain-rate estimation of the heart in vivo by means of a single ultrasound acquisition has been shown to be feasible. In this paper, the new techniques of ultrasonic strain rate and strain imaging of the heart are reviewed in terms of definitions, data acquisition, strain-rate estimation, postprocessing, and parameter extraction. Their clinical validation and relevance will be discussed using clinical examples on relevant cardiac pathology. Based on these examples, suggestions are made for future developments of these techniques.