Coherent Plane Wave Compounding combined with Euclidean distance transform for high frame rate and high contrast ultrafast imaging.

Ultrasound Coherent Plane Wave Compounding (CPWC) is a beamforming technique that generally provides higher image contrast compared to single-angle plane wave imaging (PWI). However, when a reduced number of compounding angles is used to achieve higher frame rates, the contrast may degrade due to artifacts such as grating lobes, sidelobes, and ghost artifacts. In this study, our objective is to improve the image contrast in CPWC imaging while using a lower number of transmissions, reaching a higher frame rate and high contrast CPWC image.

Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network.

Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepresented. Traditional oversampling techniques, which simply duplicate original data, have limited effectiveness in addressing this issue. To overcome these limitations, this study introduces a novel supervised autoencoder generative adversarial network (SA-GAN) for data augmentation, leveraging advanced generative artificial intelligence (AI) to create high-quality synthetic samples for minority classes.

A Novel 2x2D Radial Basis Functions-based Interpolation for Short Acquisition Time and Relaxed Frame Rate Ultrasound Localization Microscopy.

Ultrasound localization microscopy (ULM) has become a potent technique for microvascular imaging using ultrasound waves. However, one major challenge is the high frame rate and lengthy acquisition time needed to produce super-resolved (SR) images. To overcome this, our goal is to relax the frame rate and shorten this acquisition time while preserving SR image quality, thereby enhancing ULM's clinical applicability.

Mitigating high frame rate demands in shear wave elastography using radial basis function-based reconstruction: An experimental phantom study.

Background: Shear wave elastography (SWE) is a technique that quantifies tissue stiffness by assessing the speed of shear waves propagating after being excited by acoustic radiation force. SWE allows the quantification of elastic tissue properties and serves as an adjunct to conventional ultrasound techniques, aiding in tissue characterization. To capture this transient propagation of the shear wave, the ultrasound device must be able to reach very high frame rates.

Automatic Segmentation of 2-D Echocardiography Ultrasound Images by Means of Generative Adversarial Network.

Automated cardiac segmentation from 2-D echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium (LA), in order to accurately determine clinical indices.

Time Efficient Ultrasound Localization Microscopy Based on A Novel Radial Basis Function 2D Interpolation.

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images.

Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression.

Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level.

A two-dimensional angular interpolation based on radial basis functions for high frame rate ultrafast imaging.

To solve the problem of reduced image quality in plane wave imaging (PWI), coherent plane wave compounding (CPWC) has been introduced, based on a combination of plane wave images from several directions (i.e., with different angles).

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level.

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated.

Increasing frame rate of echocardiography based on a novel 2D spatio-temporal meshless interpolation.

Background: Increasing temporal resolution through numerical methods aids clinicians to evaluate fast moving structures of the heart with more confidence.

Methodology: In this study, a spatio-temporal numerical method is proposed to increase the frame rate based on two-dimensional (2D) interpolation. More specifically, we propose a novel intensity variation time surface (IVTS) strategy to incorporate both temporal and spatial information in the reconstruction.

High Frame Rate Ultrasound Imaging by Means of Tensor Completion: Application to Echocardiography.

High frame rate ultrasound (US) imaging enables the monitoring of fast-moving organs. In echocardiography, this is especially needed due to the existence of rapidly moving structures, such as the heart valves. In the last two decades, various methods have been proposed to improve the frame rate.

Efficient synthetic transmit aperture ultrasound based on tensor completion.

One of the most important methods in medical ultrasound imaging is the synthetic transmit aperture (STA). Despite the image quality improvement in the STA, this method suffers from several limitations, including a limited data acquisition rate and an increase in the overall time to form a single frame. Tensor completion (TC) is a powerful technique that uses rank minimization to recover missing information from a low-rank tensor.

Coherent Plane Wave Compounding Combined With Tensor Completion Applied for Ultrafast Imaging.

To solve the problem of resolution and contrast in plane wave imaging (PWI), coherent plane wave compounding (CPWC) was introduced, in which scanning was performed at different angles, which can achieve the desired image quality by combining the images obtained from PWI at different angles. However, the application of this idea reduces the frame rate in proportion to the number of plane waves (PWs) or angles, so that in this modality, when dealing with some applications such as shear wave imaging (SWI) and strain imaging, there is always a compromise between the frame rate and the image quality. Tensor completion (TC) is a powerful technique to recover missing information of a low-rank tensor from limited observations based on rank minimization.

Classification of sleep apnea using EMD-based features and PSO-trained neural networks.

In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima.