Fast Marching based Tissue Adaptive Delay Estimation for Aberration Corrected Delay and Sum Beamforming in Ultrasound Imaging.

Plane-wave imaging (PWI) is a best way to improve the frame rate in ultrasound imaging. However, due to the lack of transmit focus in PWI, the image quality is poor. A novel coherent plane-wave compounding (CPWC) was proposed to handle this issue, but the increased plane waves (PWs) reduced the frame rate. Thus, we combine a novel Double-Stage Delay Multiply and Sum (DS-DMAS) beamformer, which is shown to be better to improve the image quality, with plane-wave compounding to analyze the performance in terms of resolution and contrast. The simulation tests are carried out to show that applying DS-DMAS beamformer can achieve better results even if the number of transmitted PWs are decreased.

Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

For phased-array ultrasound imaging, alternative beamforming techniques and their VLSI circuits are studied to form a fully digital receive front-end hardware. In order to increase the timing accuracy in beamforming, a computationally efficient interpolation scheme to increase the sampling rate is examined. For adaptive beamforming, a phase aberration correction method with very low computational complexity is described. Image quality performance of the method is examined by processing the non-aberrated and aberrated phased-array experimental data sets of an ultrasound resolution phantom. A digital beamforming scheme based on receive focusing at the raster focal points is examined. The sector images of the resolution phantom, reconstructed from the phased-array experimental data by beamforming at the radial and raster focal points, are presented for comparison of the image resolution performances of the two beamforming schemes. VLSI circuits and their implementations for the proposed techniques are presented.

Applying the Capon adaptive beamformer in medical ultrasound imaging results in enhanced resolution by improving the interference-suppressing capabilities of the array. This improvement comes at the expense of an increased computational complexity. We have investigated the application of a beamspace adaptive beamformer for medical ultrasound imaging, which can be used to achieve reduced computational complexity with performance comparable to that of the Capon beamformer. The idea behind beamspace beamforming is that, instead of using the spatial statistics of the elements in the array to differentiate between signals and interference, we use the spatial statistics of a set of orthogonal beams, which are formed in different directions. This represents a shift from element space to beamspace. Because the majority of interference in medical ultrasound imaging is constrained to a limited spatial interval due to the focused transmit beam, this latter space can be reduced to a dimension that is lower than that of element space. We show, using simulations and experimental data, that this dimension can be selected as low as 3 while still achieving performance comparable to its element space counterpart.

Unified pixel-based (PB) beamforming has previously been implemented for ultrasound imaging. It was shown to offer significant enhancements in lateral resolution compared to conventional dynamic focusing. Yet, there is still interference from clutter and high sidelobe artefacts that limit the the contrast resolution. In this paper, we propose a combination of the unified pixel-based approach with filtered delay multiply and sum (F-DMAS) as a way of further improving the image quality. This hybrid strategy leverages the spatial coherence among signals collected from regions far from the focal depth. As a result, it helps suppress artefacts from the sidelobes and clutter, which can enhance the contrast resolution on the generated image. The new beamformer is evaluated on both simulation and data from an experimental study. The results show that the proposed approach can significantly improve the lateral resolution and image contrast over the unified PB beamformer. Compared to the F-DMAS, it maintains the high contrast resolution while generating uniform brightness over the entire imaging region.

Digital delay and sum beamforming is the promising technique for underwater 3D acoustical imaging. Even though time domain delay and sum beamforming is widely used for medical ultrasound imaging, it is hardly used for underwater acoustical 3D imaging. The conventional narrowband transmit coupled with the computational complexity required for the implementation of interpolation filter is the major reason for that. However, with the increasing popularity of wideband and ultrawideband transmission for achieving high resolution imaging and with the availability of efficient software interpolation libraries in modern computing systems, the computational time required for frequency domain beamforming appears to be more than that of time domain beamforming. Thus, through this work, we have attempted to compare the frequency domain vs time domain delay and sum beamforming for wideband underwater 3D acoustical imaging. The simulation findings demonstrated that time domain DAS beamforming is faster, computationally effective, memory-efficient, and offers somewhat superior image quality.

Application of condition coherence factor based on truncated composite method in ultrasound imaging

In recent years, ultrasound has been increasingly used to guide needle insertion procedures for spinal anesthesia. The primary anatomical targets are facet joints and epidural spaces. For these procedures, accurate visualization of the spine anatomy is of critical importance. Challenges arising from the interactions between the ultrasound beam and spinal structures including tilt caused by specular reflections, off-axis interference, and reverberations often result in weakened and blurred vertebra surfaces. Previously, adaptive beamforming methods have been proposed to improve the resolution and contrast. However, most of these methods are not specialized for improving the contrast of specular targets like bones. In this paper, we propose an accumulated angle factor (AAF)-based beamforming method customized for bone surface enhancement. This approach applies a Hilbert transform on delay compensated channel data across the receive aperture. The accumulated phase change across the receive aperture is then calculated and utilized as the weight in the beamforming output. We compared our method with classical delay and sum (DAS) beamforming method and adaptive beamforming methods such as Wiener, phase coherence factor (PCF), CF, and generalized CF (GCF) beamforming. In 12 volunteer data sets, the mean contrast ratio between the vertebrae surface and the surrounding tissue for DAS, Wiener, PCF, CF, GCF, and the proposed AAF methods are 0.49, 0.64, 0.82, 0.77, 0.76, and 0.91, respectively. The contrast is significantly improved in the proposed method.

Real-time imaging and geometric characterization of laser ultrasound based on array scanning optimization and delay-multiply-and-sum

Several high frame-rate ultrasound imaging techniques have been recently proposed which allow to increase image acquisition rates by transmitting multiple focused beams simultaneously or defocused waves, and reconstructing multiple scan lines in parallel. These methods, which include for example multi-line transmission, plane/diverging wave imaging, and parallel beamforming, have proved to be successful in increasing the frame-rate, but on the other hand they have also showed some limitations in terms of the achievable image quality, partly degrading image contrast and resolution. Different solutions have been proposed to address this problem so far, such as the use of advanced receive beamforming algorithms able to improve the beam shape and the quality of the obtained image. Among these, beamformers based on backscattered signals spatial coherence, as e.g. Filtered Delay Multiply and Sum beamforming, Coherence-Factor-based methods, and Short Lag Spatial Coherence Imaging, have gained increasing attention for their ability to enhance image contrast and suppress clutter. In this presentation, an overview of some recent results obtained by jointly exploiting high frame-rate ultrasound imaging and coherence-based beamforming methods will be presented, showing through experimental tests the performance improvement achievable with these techniques.

A new adaptive delay estimation algorithm with variable step size based upon a quadrature phase detector and a fractional delay filter (FDF) is introduced. The estimator improves the convergence rate of the recently proposed adaptive quadrature delay estimator (QDE). This convergence parameter is a function of the phase error. Simulation results verify the improved performance in terms of convergence rate and stability, especially when the convergence factor mu is small and SNR is low.

Gradient based algorithms for adaptive estimation of fractional time delay are known to suffer from lock-up phenomena to some extend. This is mainly due to errors in the gradient estimate which in turn is caused by interpolation errors. By studying the expected behavior of a class of stochastic descent algorithms, we are able to show that by replacing derivatives with finite differences, lock-up can be prevented. The approach is validated by deriving a robust estimator which combines subsample accuracy with excellent convergence properties in a computational efficient way.

This paper presents a new beamforming method for real-time three-dimensional (3-D) ultrasound imaging using a 2-D matrix transducer. To obtain images with sufficient resolution and contrast, several thousand elements are needed. The proposed method reduces the required channel count from the transducer to the main imaging system, by including electronics in the transducer handle. The reduction of element channel count is achieved using a sequential beamforming scheme. The beamforming scheme is a combination of a fixed focus beamformer in the transducer and a second dynamic focus beamformer in the main system. The real-time imaging capability is achieved using a synthetic aperture beamforming technique, utilizing the transmit events to generate a set of virtual elements that in combination can generate an image. The two core capabilities in combination is named Synthetic Aperture Sequential Beamforming (SASB). Simulations are performed to evaluate the image quality of the presented method in comparison to Parallel beamforming utilizing 16 receive beamformers. As indicators for image quality the detail resolution and Cystic resolution are determined for a set of scatterers at a depth of 90mm for elevation and azimuth angles from 0° to 25°. Simulations show that the acoustic performance of the Proposed method is less angle dependent than Parallel beamforming. The Cystic resolution is shown to be more than 50% improved, with a detail resolution on the same order as Parallel Beamforming.

Image-based automatic breast tumor detection has become a significant research focus, driven by recent advancements in machine learning (ML) algorithms. Traditional disease detection methods often involve manual feature extraction from images, a process requiring extensive expertise from specialists and pathologists. This labor-intensive approach is not only time-consuming but also impractical for widespread application. However, advancements in digital technologies and computer vision have enabled convolutional neural networks (CNNs) to learn features automatically, thereby overcoming these challenges. This paper presents a deep neural network model based on the MobileNet-V2 architecture, enhanced with a convolutional block attention mechanism for identifying tumor types in ultrasound images. The attention module improves the MobileNet-V2 model’s performance by highlighting disease-affected areas within the images. The proposed model refines features extracted by MobileNet-V2 using the Nesterov-accelerated Adaptive Moment Estimation (Nadam) optimizer. This integration enhances convergence and stability, leading to improved classification accuracy. The proposed approach was evaluated on the BUSI ultrasound image dataset. Experimental results demonstrated strong performance, achieving an accuracy of 99.1%, sensitivity of 99.7%, specificity of 99.5%, precision of 97.7%, and an area under the curve (AUC) of 1.0 using an 80–20 data split. Additionally, under 10-fold cross-validation, the model achieved an accuracy of 98.7%, sensitivity of 99.1%, specificity of 98.3%, precision of 98.4%, F1-score of 98.04%, and an AUC of 0.99.

The synthetic aperture focusing technique (SAFT) has been proposed to improve the lateral resolution of ultrasound image outside the focal region of the transducer. Yet, SAFT would result in an unexpected artifact image at the region near the focal point of the transducer due to the use of a virtue source concept associated with limited numbers of scan lines. To further improve this issue, the present study incorporated the transducer’s depth-dependency beam characteristics into SAFT-based imaging procedure. The imaging process was implemented on a graphic processing unit (GPU) platform for improving the computational efficiency. Ultrasound images were acquired from a commercial phantom with various contrast regions using a 3.5 MHz single element transducer. The -20dB beam width of transducer as a function of axial depth were measured for better estimating the time delay of each scanning point. Two scanning intervals, 50 and 200 μm, were acquired for B-mode imaging. Subsequently, the linear interpolation was performed for correcting the error associated with the time delay estimator for the image at the focus region. Results demonstrated that SAFT-based images implemented with virtual source correction corresponding to scanning intervals of 50 and 200 μm both displayed darker regions near the depth of 23 mm. The SAFT-based images modified with the correction of -20dB beam width of the transducer may not only improve the artifact region but also better restore the shape and contrast regions in the deeper regions of phantom. In additional, the computational efficiency is verified able to be greatly improved by the implementation with GPU platform. These results verify that current modified SAFT-based imaging may greatly enhance ultrasound image quality.KeywordsModified synthetic aperture focusing techniqueVirtual sourceBeam widthTransducer characteristics