High frame rate multi-perspective cardiac ultrasound imaging using phased array probes

Ultrasound (US) imaging is used to assess cardiac disease by assessing the geometry and function of the heart utilizing its high spatial and temporal resolution. However, because of physical constraints, drawbacks of US include limited field-of-view, refraction, resolution and contrast anisotropy. These issues cannot be resolved when using a single probe. Here, an interleaved multi-perspective 2-D US imaging system was introduced, aiming at improved imaging of the left ventricle (LV) of the heart by acquiring US data from two separate phased array probes simultaneously at a high frame rate. In an ex-vivo experiment of a beating porcine heart, parasternal long-axis and apical views of the left ventricle were acquired using two phased array probes. Interleaved multi-probe US data were acquired at a frame rate of 170 frames per second (FPS) using diverging wave imaging under 11 angles. Image registration and fusion algorithms were developed to align and fuse the US images from two different probes. First- and second-order speckle statistics were computed to characterize the resulting probability distribution function and point spread function of the multi-probe image data. First-order speckle analysis showed less overlap of the histograms (reduction of 34.4%) and higher contrast-to-noise ratio (CNR, increase of 27.3%) between endocardium and myocardium in the fused images. Autocorrelation results showed an improved and more isotropic resolution for the multi-perspective images (single-perspective: 0.59 mm × 0.21 mm, multi-perspective: 0.35 mm × 0.18 mm). Moreover, mean gradient (MG) (increase of 74.4%) and entropy (increase of 23.1%) results indicated that image details of the myocardial tissue can be better observed after fusion. To conclude, interleaved multi-perspective high frame rate US imaging was developed and demonstrated in an ex-vivo experimental setup, revealing enlarged field-of-view, and improved image contrast and resolution of cardiac images.</p

Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A Low-Rank-Topology-Preserving Approach

Cardiac motion estimation is an important diagnostic tool to detect heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of the complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate the cardiac motion using ultrafast ultrasound data. -- Our solution is based on a variational formulation characterized by the L2-regularized class. The displacement is represented by a lattice of b-splines and we ensure robustness by applying a maximum likelihood type estimator. While this is an important part of our solution, the main highlight of this paper is to combine a low-rank data representation with topology preservation. Low-rank data representation (achieved by finding the k-dominant singular values of a Casorati Matrix arranged from the data sequence) speeds up the global solution and achieves noise reduction. On the other hand, topology preservation (achieved by monitoring the Jacobian determinant) allows to radically rule out distortions while carefully controlling the size of allowed expansions and contractions. Our variational approach is carried out on a realistic dataset as well as on a simulated one. We demonstrate how our proposed variational solution deals with complex deformations through careful numerical experiments. While maintaining the accuracy of the solution, the low-rank preprocessing is shown to speed up the convergence of the variational problem. Beyond cardiac motion estimation, our approach is promising for the analysis of other organs that experience motion.Comment: 15 pages, 10 figures, Physics in Medicine and Biology, 201

Head to head comparison of 2D vs real time 3D dipyridamole stress echocardiography

Real-time three-dimensional (RT-3D) echocardiography has entered the clinical practice but true incremental value over standard two-dimensional echocardiography (2D) remains uncertain when applied to stress echo. The aim of the present study is to establish the additional value of RT-3D stress echo over standard 2D stress echocardiography. We evaluated 23 consecutive patients (age = 65 ± 10 years, 16 men) referred for dipyridamole stress echocardiography with Sonos 7500 (Philips Medical Systems, Palo, Alto, CA) equipped with a phased – array 1.6–2.5 MHz probe with second harmonic capability for 2D imaging and a 2–4 MHz matrix-phased array transducer producing 60 × 70 volumetric pyramidal data containing the entire left ventricle for RT-3D imaging. In all patients, images were digitally stored in 2D and 3D for baseline and peak stress with a delay between acquisitions of less than 60 seconds. Wall motion analysis was interpreted on-line for 2D and off-line for RT-3D by joint reading of two expert stress ecocardiographist. Segmental image quality was scored from 1 = excellent to 5 = uninterpretable. Interpretable images were obtained in all patients. Acquisition time for 2D images was 67 ± 21 sec vs 40 ± 22 sec for RT-3D (p = 0.5). Wall motion analysis time was 2.8 ± 0.5 min for 2D and 13 ± 7 min for 3D (p = 0.0001). Segmental image quality score was 1.4 ± 0.5 for 2D and 2.6 ± 0.7 for 3D (p = 0.0001). Positive test results was found in 5/23 patients. 2D and RT-3D were in agreement in 3 out of these 5 positive exams. Overall stress result (positive vs negative) concordance was 91% (Kappa = 0.80) between 2D and RT-3D. During dipyridamole stress echocardiography RT-3D imaging is highly feasible and shows a high concordance rate with standard 2D stress echo. 2D images take longer time to acquire and RT-3D is more time-consuming to analyze. At present, there is no clear clinical advantage justifying routine RT-3D stress echocardiography use

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research