Cardiac Valve Event Timing in Echocardiography Using Deep Learning and Triplane Recordings.

Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES).

Automated Segmentation and Quantification of the Right Ventricle in 2-D Echocardiography.

Objective: The right ventricle receives less attention than its left counterpart in echocardiography research, practice and development of automated solutions. In the work described here, we sought to determine that the deep learning methods for automated segmentation of the left ventricle in 2-D echocardiograms are also valid for the right ventricle. Additionally, here we describe and explore a keypoint detection approach to segmentation that guards against erratic behavior often displayed by segmentation models.

Real-Time Echocardiography Guidance for Optimized Apical Standard Views.

Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2-D ultrasound imaging. The reliability of these measurements depends on the correct pose of the transducer such that the 2-D imaging plane properly aligns with the heart for standard measurement views and is thus dependent on the operator's skills. We propose a deep learning tool that suggests transducer movements to help users navigate toward the required standard views while scanning.

User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing.

Spectral Doppler measurements are an important part of the standard echocardiographic examination. These measurements give insight into myocardial motion and blood flow, providing clinicians with parameters for diagnostic decision making. Many of these measurements are performed automatically with high accuracy, increasing the efficiency of the diagnostic pipeline.

3D Myocardial Mechanical Wave Measurements: Toward In Vivo 3D Myocardial Elasticity Mapping.

Objectives: This study aimed to investigate the potential of a novel 3-dimensional (3D) mechanical wave velocity mapping technique, based on the natural mechanical waves produced by the heart itself, to approach a noninvasive 3D stiffness mapping of the left ventricle.

Background: Myocardial fibrosis is recognized as a pathophysiological substrate of major cardiovascular disorders such as cardiomyopathies and valvular heart disease. As fibrosis leads to increased myocardial stiffness, ultrasound elastography measurements could provide important clinical information.

Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning.

Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. However, the ECG signal is not always recorded. In such cases, the cardiac cycles can be estimated manually from the ultrasound data retrospectively.

Clutter Filter Wave Imaging.

The elastic properties of human tissue can be evaluated through the study of mechanical wave propagation captured using high frame rate ultrasound imaging. Methods such as block-matching or phase-based motion estimation have been used to estimate the displacement induced by the mechanical waves. In this paper, a new method for detecting mechanical wave propagation without motion estimation is presented, where the motion of interest is accentuated by an appropriate clutter filter.

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks.

Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, and it is essential that the morphophysiological representations are correct. Clinical analysis is often initialized with the current view, and automatic classification can thus be useful in improving today's workflow.

Myocardial Strain Rate by Anatomic Doppler Spectrum: First Clinical Experience Using Retrospective Spectral Tissue Doppler from Ultra-High Frame Rate Imaging.

Strain rate imaging by tissue Doppler (TDI) is vulnerable to stationary reverberations and noise (clutter). Anatomic Doppler spectrum (ADS) presents retrospective spectral Doppler from ultra-high frame rate imaging (UFR-TDI) data for a region of interest, that is, ventricular wall or segment, at one time instance. This enables spectral assessment of strain rate (SR) without the influence of clutter.

Strain rate imaging combined with wall motion analysis gives incremental value in direct quantification of myocardial infarct size.

Background: The study aimed to evaluate the diagnostic accuracy of a new method for direct echocardiographic quantification of the myocardial infarct size, using late enhancement magnetic resonance imaging (LE-MRI) as a reference method.

Methods And Results: Echocardiography and LE-MRI were performed on average 31 days after first-time myocardial infarction in 58 patients. Echocardiography was also performed on 35 healthy controls.

Echocardiography without electrocardiogram.

Aims: automatic detection of the QRS complex on electrocardiogram (ECG) is used on cardiac ultrasound scanners to separate ultrasound image series into cardiac cycles for playback and storage. On small hand-held scanners it is unpractical to connect ECG cables. We therefore aim to do automatic cardiac cycle separation using apical B-mode ultrasound images.

Reproducibility in echocardiographic assessment of the left ventricular global and regional function, the HUNT study.

Aims: The study aimed to compare the inter-observer reproducibility of new and traditional measurements of the left ventricular (LV) global and regional function.

Methods And Results: Two experienced echocardiographers performed 20 complete echo/Doppler examinations and 50 analyses on ten healthy subjects. All recordings were analysed for systolic and diastolic conventional and deformation measurements by both echocardiographers.

Automatic timing of aortic valve closure in apical tissue Doppler images.

Ultrasound color tissue Doppler imaging (TDI) can be used to estimate velocities of moving left ventricular cardiac tissue. Aortic valve closure (AVC) can be observed as a notch in apical TDI velocity/time curves occurring after ejection, but before early relaxation. This work sought to evaluate automatic and automated algorithms using TDI for timing AVC.

Automated analysis of strain rate and strain: feasibility and clinical implications.

Background: This study evaluated 3 new automated methods, based on a combination of speckle tracking and tissue Doppler, for the analysis of strain rate (SR) and strain. Feasibility and values for peak systolic strain rate (SR s ) and end-systolic strain (S es ) were assessed.

Methods: Thirty patients with myocardial infarction and 30 normal subjects were examined.