Ultrasound Plane Wave Imaging Research Articles

Improved subarray coherence factor beamformer for ultrasound plane-wave imaging

Abstract Coherent plane-wave compounding (CPWC) has gained significant traction in medical ultrasound imaging. The coherence factor (CF) weighting algorithm, a classical adaptive beamforming strategy, aims to enhance the CPWC imaging lateral resolution, but it also noticeably compromises the speckle patterns, particularly.at a low signal-to-noise ratio (SNR). Delay multiply and sum (DMAS) has been proposed to reduce side lobes. However, the reconstructed image quality with DMAS is limited in terms of resolution and speckle level. In the present study, an improved subarray coherence factor (ISCF) algorithm is introduced by combining CF with DMAS strategies, aiming to improve the contrast and resolution while preserving the speckle level. The performance of the proposed algorithm is evaluated using simulation and phantom datasets. Results indicate that the resolution and the contrast of ISCF can be improved by 41.6% and 79.4%, respectively; compared with delay and sum (DAS) algorithm. Additionally, the resolution and the speckle level of ISCF can be improved by 10.6% and 35.7% compared with CF algorithm, respectively. The proposed ISCF algorithm outperforms CF in terms of resolution, contrast, and speckle preservation.

A modified sidelobe blanking beamforming method for ultrasound plane wave imaging.

In this paper, an ultrasound beamforming method for plane wave (PW) imaging based on modified sidelobe blanking (MSLB) is proposed to improve image resolution and contrast ratio (CR). In this framework, PWs from various angles were designed to create main and auxiliary beamformer signals. Specifically, the PW signals from all angles were first coherently combined to serve as the main beamformer output signals. To prevent excessive clutter and noise, output signals in the main beamformer were weighted by the generalized coherence factor. Subsequently, the PW signals were split into positive and negative angles to perform a subtraction, creating the auxiliary beamformer. Finally, signals in the main beamformer were compared with the signals in the auxiliary beamformer point by point to further eliminate the noises and clutters. Compared with the delay and sum, full width at half maximum of the MSLB for point targets was reduced by an average of 54.17% and 51.65% in simulations and experiments, respectively; and the corresponding CR was improved by 55.38% and 18.40% on average. The MSLB method provided better imaging quality in human carotid arteries. In conclusion, the proposed method can effectively improve image resolution and CR with low computational complexity.

A transcranial multiple waves suppression method for plane wave imaging based on Radon transform

Transcranial ultrasound imaging presents a significant challenge due to the intricate interplay between ultrasound waves and the heterogeneous human skull. The skull’s presence induces distortion, refraction, multiple scattering, and reflection of ultrasound signals, thereby complicating the acquisition of high-quality images. Extracting reflections from the entire waveform is crucial yet exceedingly challenging, as intracranial reflections are often obscured by strong amplitude direct waves and multiple scattering. In this paper, a multiple wave suppression method for ultrasound plane wave imaging is proposed to mitigate the impact of skull interference. Drawing upon prior research, we developed an enhanced high-resolution linear Radon transform using the maximum entropy principle and Bayesian method, facilitating wavefield separation. We detailed the process of wave field separation in the Radon domain through simulation of a model with a high velocity layer. When plane waves emitted at any steering angles, both multiple waves and first arrival waves manifested as distinct energy points. In the brain simulation, we contrasted the characteristic differences between skull reflection and brain-internal signal in Radon domain, and demonstrated that multiples suppression method reduces side and grating lobe levels by approximately 30 dB. Finally, we executed in vitro experiments using a monkey skull to separate weak intracranial reflection signals from strong skull reflections, enhancing the contrast-to-noise ratio by 85 % compared to conventional method using full waveform. This study deeply explores the effect of multiples on effective signal separation, addresses the complexity of wavefield separation, and verifies its efficacy through imaging, thereby significantly advancing ultrasound transcranial imaging techniques.

Ultrasound-tensiometry: A new method for measuring differential loading within a tendon during movement

BackgroundTendons transmit tensile load from muscles to the skeleton. Differential loading across a tendon can occur, especially when it contains subtendons originating from distinct muscles. Tendon shear wave speed has previously been shown to reflect local tensile stress. Hence, a tool that measures spatial variations in wave speed may reflect differential loading within a tendon during human movement. Research questionDo wave speeds measured via high-framerate ultrasound-based tensiometry correspond with differential loading across a tendon? MethodsUltrasound-tensiometry uses an external mechanical actuator to induce waves and high-framerate plane wave ultrasound imaging (20 kHz) to track tissue displacements arising from wave propagation within a tendon. Local tissue displacements are temporally and spatially filtered to remove high-frequency noise and reflected waves. A Radon transform of the spatio-temporal displacement data is used to compute the shear wave speed across the tendon.We evaluated ultrasound-tensiometry’s ability to measure differential loading across a tendon using in silico, ex vivo and in vivo approaches. The in silico approach used a finite element model to simulate wave propagation along two adjacent subtendons undergoing differential loading. The ex vivo experiment measured wave speed in adjacent porcine flexor subtendons subjected to differential loading. In vivo, we tracked wave speed across the Achilles tendon while a participant performed calf stretches to differentially load the subtendons, and while walking on a treadmill at 1.5 m/s. ResultsWave speeds modulated with local tendon stress under both in silico and ex vivo conditions, with higher wave speeds observed in subtendons subjected to higher loads (6–16 m/s higher at 1.5× load differential). Spatial variations in in vivo Achilles tendon wave speeds were consistent with differential subtendon loading arising from distinct muscle loads (maximum range: 0–137 m/s, resolution: 0.1 mm×0.2 mm, precision: ±0.2 m/s). SignificanceHigh-framerate ultrasound-tensiometry tracks spatial variations in tendon wave speed, which may be useful to investigate local tissue loading and to delineate individual muscle contributions to movement.

Improved Transcranial Plane-Wave Imaging With Learned Speed-of-Sound Maps.

Although transcranial ultrasound plane-wave imaging (PWI) has promising clinical application prospects, studies have shown that variable speed-of-sound (SoS) would seriously damage the quality of ultrasound images. The mismatch between the conventional constant velocity assumption and the actual SoS distribution leads to the general blurring of ultrasound images. The optimization scheme for reconstructing transcranial ultrasound image is often solved using iterative methods like full-waveform inversion. These iterative methods are computationally expensive and based on prior magnetic resonance imaging (MRI) or computed tomography (CT) information. In contrast, the multi-stencils fast marching (MSFM) method can produce accurate time travel maps for the skull with heterogeneous acoustic speed. In this study, we first propose a convolutional neural network (CNN) to predict SoS maps of the skull from PWI channel data. Then, use these maps to correct the travel time to reduce transcranial aberration. To validate the performance of the proposed method, numerical, phantom and intact human skull studies were conducted using a linear array transducer (L11-5v, 128 elements, pitch = 0.3 mm). Numerical simulations demonstrate that for point targets, the lateral resolution of MSFM-restored images increased by 65%, and the center position shift decreased by 89%. For the cyst targets, the eccentricity of the fitting ellipse decreased by 75%, and the center position shift decreased by 58%. In the phantom study, the lateral resolution of MSFM-restored images was increased by 49%, and the position shift was reduced by 1.72 mm. This pipeline, termed AutoSoS, thus shows the potential to correct distortions in real-time transcranial ultrasound imaging, as demonstrated by experiments on the intact human skull.

A New Method of Plane-Wave Ultrasound Imaging Based on Reverse Time Migration.

Coherent plane-wave compounding technique enables rapid ultrasound imaging with comparable image quality to traditional B-mode imaging that relies on focused beam transmission. However, existing methods assume homogeneity in the imaged medium, neglecting the heterogeneity in sound velocities and densities present in real tissues, resulting in noise reverberation. This study introduces the Reverse Time Migration (RTM) method for ultrasound plane-wave imaging to overcome this limitation, which is combined with a method for estimating the speed of sound in layered media. Simulation results in a homogeneous background demonstrate that RTM reduces side lobes and grating lobes by approximately 30 dB, enhancing the contrast-to-noise ratio by 20% compared to conventional delay and sum (DAS) beamforming. Moreover, RTM achieves superior imaging outcomes with fewer compounding angles. The lateral resolution of the RTM with 5-9 angle compounding is able to achieve the effectiveness of the DAS method with 15-19 angle compounding, and the CNR of the RTM with 11-angle compounding is almost the same as that of the DAS with 21-angle compounding. In a heterogeneous background, experimental simulations and in vitro wire phantom experiments confirm RTM's capability to correct depth imaging, focusing reflected waves on point targets. In vitro porcine tissue experiments enable accurate imaging of layer interfaces by estimating the velocities of multiple layers containing muscle and fat. The proposed imaging procedure optimizes velocity estimation in complex media, compensates for the impact of velocity differences, provides more reliable imaging results.

Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models.

Ultrasound plane wave imaging is a cutting-edge technique that enables high frame-rate imaging. However, one challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Therefore, the development of a denoising method becomes imperative to augment the quality of plane wave images. Drawing inspiration from Denoising Diffusion Probabilistic Models (DDPMs), our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise and effectively eliminates it by adapting a DDPM to beamformed radiofrequency (RF) data. The method underwent training using only 400 simulated images. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes. The proposed method was assessed across simulation, phantom, and in vivo images. The results of the evaluations indicate that our approach not only enhances image quality on simulated data but also demonstrates effectiveness on phantom and in vivo data in terms of image quality. Comparative analysis with other methods underscores the superiority of our proposed method across various evaluation metrics. The source code and trained model will be released along with the dataset at: http://code.sonography.ai.

Minimum Variance Optimizations with Different Covariance Matrices in Plane-Wave Ultrasound Imaging

Minimum Variance Optimizations with Different Covariance Matrices in Plane-Wave Ultrasound Imaging

WSRGAN: A wavelet-based GAN for super-resolution of plane-wave ultrasound images without sampling loss

Plane-wave ultrasound imaging has ultra-fast temporal resolution and can image at frame rates exceeding 1 kHz, enabling breakthrough technologies like shear wave elastography. However, the imaging process of this technology lacks focus and poor imaging quality and usually requires beamforming technology to assist imaging, such as Coherent Plane-Wave Compounding (CPWC). The imaging quality of this method is often directly related to the number of beams, sacrificing the time advantage of single-beam plane wave imaging. This study proposes a Wavelet-based Generative Adversarial Network for Super-Resolution (WSRGAN) to meet the growing demand for high-quality imaging of single-beam plane waves. WSRGAN uses the encoding and decoding network as the generator and uses wavelet transform to replace the sampling process. Furthermore, we further designed adaptive wavelet attention to enable the model to focus attention on different levels of features at different stages. Wavelet GAN loss was proposed as GAN loss, and a new combined loss function was designed for the generator. The experiment was conducted on the Plane-wave Imaging Challenge in Medical UltraSound (PICMUS) 2016 dataset. On the point target, the Full Width at Half Maximum (FWHM) of WSRGAN reached 0.268 mm to 0.502 mm. On the cyst target, the contrast of WSRGAN reaches 26.156 dB to 37.223 dB. Among invivo targets, the Peak Signal-to-Noise Ratio (PSNR) of WSRGAN is 41.518 dB, and the Structural Similarity Index Measurement (SSIM) is 0.984. Experiments show that each module proposed by the model has a total contribution and is helpful for various experiments. All targets performed well.

Ultrasound Plane-Wave Compressed Sensing Reconstruction Method Using Intra-frame and Inter-frame Joint Multi-hypothesis and Reference Frame Multi-hypothesis Predictions

Ultrasound plane-wave imaging has the advantage of high frame rate in addition to high data volume. High data sampling rates and large amounts of data storage can become bottlenecks in ultrasound system design. Although compressed sensing technology can help reduce the burden of sampling and transmission, it achieves relatively low image quality because of its reliance solely on signal sparsity. Therefore, we proposed reconstructing the ultrasound signal by applying additional prior knowledge, such as plane-wave imaging and its echo characteristics. Inspired by multi-hypothesis prediction methods in video compression coding, the plane-wave multi-hypothesis prediction compressed sensing reconstruction method was proposed to improve the accuracy of reconstructions. We applied multi-hypothesis prediction and residual reconstruction on the plane wave to enhance the quality of reconstruction and correct predicted values. Also, to acquire high-quality hypotheses, two hypothesis acquisition schemes were evaluated, constructing search windows on both preceding and subsequent frames as well as the reference frame. Compared with traditional reconstruction methods that rely on sparsity, multi-hypothesis prediction compressed sensing methods can reduce signal reconstruction errors and significantly eliminate image artifacts. Furthermore, by using improved hypotheses, signal reconstruction and image quality can be enhanced, resulting in higher contrast. Comparative simulation experimental results based on the publicly available Plane-Wave Imaging Challenge in Medical Ultrasound (PICMUS) and acoustic radiation force imaging data sets demonstrate that the proposed method outperforms other methods in both reconstruction errors and image quality. This helps to reduce the complexity of sampling and transmission of the ultrasound system.

Cerebral microvascular markers of intracranial pressure in acute and subacute porcine hydrocephalus model

Hydrocephalus affects 1.1% of infants, resulting in abnormal accumulation of cerebrospinal fluid and elevated intracranial pressure (ICP). Elevated ICP can occur prior to detectable morphological changes using computed tomography, resulting in delayed diagnosis and treatment. To improve early diagnosis, we propose vascular imaging using ultrasound localization microscopy (ULM) and ultrafast Doppler to measure cerebral blood flow changes due to acute to subacute elevations in ICP. We performed a pre-clinical study on a neonatal porcine model with 30 min, 8 h, 12 h, and 24 h ICP elevations while performing brain ultrasound measurements in hourly intervals. Ultrafast plane-wave ultrasound imaging was implemented using the Verasonics Vantage-256 and GE IC59D curved array probe at 400Hz frame rate. Lumason contrast agent was infused into each animal, providing tracers for ULM and enhancing the ultrafast Doppler imaging. Based on preliminary analysis, ultrafast Doppler showed reduced total cerebral blood volume after acute ICP elevation. ULM showed highly correlated parabolic reductions in cortical microcirculation with increasing ICP. Drastic reductions in cerebral microcirculation but less so in microcirculation accompanied microdialysis evidence of brain ischemia. Additionally, there were spatiotemporal changes in cerebral microvascular flow with varying ICP durations that warrant additional investigations using ultrafast ultrasound imaging.

ARU-GAN: U-shaped GAN based on Attention and Residual connection for super-resolution reconstruction

Plane-wave ultrasound imaging technology offers high-speed imaging but lacks image quality. To improve the image spatial resolution, beam synthesis methods are used, which often compromise the temporal resolution. Herein, we propose ARU-GAN, a super-resolution reconstruction model based on residual connectivity and attention mechanisms, to address this issue. ARU-GAN comprises a Full-scale Skip-connection U-shaped Generator (FSUG) with an attention mechanism and a Residual Attention Patch Discriminator (RAPD). The former captures global and local features of the image by using full-scale skip-connections and attention mechanisms. The latter focuses on changes in the image at different scales to enhance its discriminative ability at the patch level. ARU-GAN was trained using a combined loss function on the Plane-Wave Imaging Challenge in Medical Ultrasound (PICMUS) 2016 dataset, which includes three types of targets: point targets, cyst targets, and in-vivo targets. Compared to Coherent Plane-Wave Compounding (CPWC), ARU-GAN achieved a reduction in Full Width at Half Maximum (FWHM) by 5.78%–20.30% on point targets, improved Contrast (CR) by 7.59–11.29 percentage points, and Contrast to Noise Ratio (CNR) by 30.58%–45.22% on cyst targets. On in-vivo target, ARU-GAN improved the Peak Signal-to-Noise Ratio (PSNR) by 11.94%, the Complex-Wavelet Structural Similarity Index Measurement (CW-SSIM) by 17.11%, and the Normalized Cross Correlation (NCC) by at least 2.17% compared to existing deep learning methods. In conclusion, ARU-GAN is a promising model for the super-resolution reconstruction of plane-wave medical ultrasound images. It provides a novel solution for improving image quality, which is essential for clinical practice.

Ray theory-based compounded plane wave ultrasound imaging for aberration corrected transcranial imaging: Phantom experiments and simulations

Compounded plane wave imaging (CPWI) allows high-frame-rate measurement and has been one of the most promising modalities for real-time brain imaging. However, ultrasonic brain imaging using the CPWI modality is usually performed with a worn thin or removal of the skull layer. Otherwise, the skull layer is expected to distort the ultrasonic wavefronts and significantly decrease intracranial imaging quality. The motivation of this study is to investigate a CPWI method for transcranial brain imaging with the skull layer. A coordinate transformation ray-tracing (CTRT) approach was proposed to track the distorted ultrasonic wavefronts and calculate the time delays for the ultrasound plane wave passing through the skull layer. With an accurate correction for the time delays in beamforming, the CTRT-based CPWI could achieve high-quality intracranial images with the presence of skulls. The proposed CTRT-based CPWI method was verified using a simplified three-layer transcranial model. The full-wave simulation demonstrated that CTRT could accurately (i.e., relative percentage error less than0.18%) track the distorted transmitting wavefront through skull. Compared with the CPWI without aberration correction, the CTRT-based CPWI provided high-quality intracranial imaging and could accurately localize intracranial point scatterers; specifically, positioning error decreases from 0.5 mm to 0.1 mm on average in the axial direction and from 0.7 mm to 0.1 mm on average in the lateral direction. As the compounded angles increased in the CTRT-based CPWI, the contrast improved by 16.2 dB on average for the region of interest, and the array performance indicator (representing resolution) decreased by 4.0 on average for the intracranial point scatterers. The CTRT is of low computational cost compared with full wave simulation. This study suggested that the proposed CTRT-based CPWI might have the potential for real-time and non-invasive transcranial aberration-corrected imaging.

Detecting Early Ocular Choroidal Melanoma Using Ultrasound Localization Microscopy.

Ocular choroidal melanoma (OCM) is the most common ocular primary malignant tumor in adults, and there is an increasing emphasis on its early detection and treatment worldwide. The main obstacle in early detection of OCM is its overlapping clinical features with benign choroidal nevus. Thus, we propose ultrasound localization microscopy (ULM) based on the image deconvolution algorithm to assist the diagnosis of small OCM in early stages. Furthermore, we develop ultrasound (US) plane wave imaging based on three-frame difference algorithm to guide the placement of the probe on the field of view. A high-frequency Verasonics Vantage system and an L22-14v linear array transducer were used to perform experiments on both custom-made modules in vitro and a SD rat with ocular choroidal melanoma in vivo. The results demonstrate that our proposed deconvolution method implement more robust microbubble (MB) localization, reconstruction of microvasculature network in a finer grid and more precise flow velocity estimation. The excellent performance of US plane wave imaging was successfully validated on the flow phantom and in an in vivo OCM model. In the future, the super-resolution ULM, a critical complementary imaging modality, can provide doctors with conclusive suggestions for early diagnosis of OCM, which is significant for the treatment and prognosis of patients.

Improved Coherent Plane-Wave Compounding Using Sign Coherence Factor Weighting for Frequency-Domain Beamforming.

This study proposes a novel modified sign coherence factor (SCF) weighting adapted to the frequency-domain (FD) beamforming for ultrasound plane-wave imaging to achieve a high frame rate and better image quality. First, before beamforming, the sign components were extracted from the radiofrequency signals of aperture data. Second, the modified SCF was established using the FD beamformed sign components. Finally, the FD beamformed image was weighted by the modified SCF. To assess the performance of the proposed modified SCF for FD beamforming, the resolution, contrast, computation complexity and execution time of the generated images were evaluated. The results revealed that the FD-SCF could significantly improve the computational load compared with the classic delay-and-sum SCF on the premise of equal image quality improvement. Therefore, high image quality and low computational load have been successfully combined under the proposed weighting method.

A Radon diffraction theorem for plane wave ultrasound imaging.

The rising demand on high frame rate ultrasound imaging applications necessitates the development of fast algorithms for plane wave image reconstruction. We introduce a new class of plane wave reconstructions that relies on a relation between receive data and image data in the Radon domain. This relation is derived for arbitrary dimensions and validated on multiple two-dimensional plane wave data sets. We further present a mathematical relation between conventional delay-and-sum and Fourier domain reconstruction methods and the method proposed. Our analysis shows that they all rely on the same physical model with slight variations in certain filtering steps and, therefore, the new Radon domain reconstruction yields similar results as other methods in terms of image quality. However, we show that our method offers a huge potential to improve computation time by reducing the number of applied projections and to improve image quality by introducing nonlinear operations in the Radon domain, e.g., for edge enhancement. As the Radon transform retains both angular and temporal information, the relation also provides new insights on the fundamentals of plane wave imaging that can be leveraged for optimizing acquisition schemes or for developing novel compounding strategies in the future.

High-Definition Plane-Wave Ultrasound Imaging Based on Subbands Phase Information Extracted From Echo Signals

High-Definition Plane-Wave Ultrasound Imaging Based on Subbands Phase Information Extracted From Echo Signals

Improving plane wave ultrasound imaging through real-time beamformation across multiple arrays

Ultrasound imaging is a widely used diagnostic tool but has limitations in the imaging of deep lesions or obese patients where the large depth to aperture size ratio (f-number) reduces image quality. Reducing the f-number can improve image quality, and in this work, we combined three commercial arrays to create a large imaging aperture of 100 mm and 384 elements. To maintain the frame rate given the large number of elements, plane wave imaging was implemented with all three arrays transmitting a coherent wavefront. On wire targets at a depth of 100 mm, the lateral resolution is significantly improved; the lateral resolution was 1.27 mm with one array (1/3 of the aperture) and 0.37 mm with the full aperture. After creating virtual receiving elements to fill the inter-array gaps, an autoregressive filter reduced the grating lobes originating from the inter-array gaps by − 5.2 dB. On a calibrated commercial phantom, the extended field-of-view and improved spatial resolution were verified. The large aperture facilitates aberration correction using a singular value decomposition-based beamformer. Finally, after approval of the Stanford Institutional Review Board, the three-array configuration was applied in imaging the liver of a volunteer, validating the potential for enhanced resolution.

Reconstruction for plane-wave ultrasound imaging using modified U-Net-based beamformer.

An image reconstruction method that can simultaneously provide high image quality and frame rate is necessary for diagnosis on cardiovascular imaging but is challenging for plane-wave ultrasound imaging. To overcome this challenge, an end-to-end ultrasound image reconstruction method is proposed for reconstructing a high-resolution B-mode image from radio frequency (RF) data. A modified U-Net architecture that adopts EfficientNet-B5 and U-Net as the encoder and decoder parts, respectively, is proposed as a deep learning beamformer. The training data comprise pairs of pre-beamformed RF data generated from random scatterers with random amplitudes and corresponding high-resolution target data generated from coherent plane-wave compounding (CPWC). To evaluate the performance of the proposed beamforming model, simulation and experimental data are used for various beamformers, such as delay-and-sum (DAS), CPWC, and other deep learning beamformers, including U-Net and EfficientNet-B0. Compared with single plane-wave imaging with DAS, the proposed beamforming model reduces the lateral full width at half maximum by 35% for simulation and 29.6% for experimental data and improves the contrast-to-noise ratio and peak signal-to-noise ratio, respectively, by 6.3 and 9.97dB for simulation, 2.38 and 3.01dB for experimental data, and 3.18 and 1.03dB for in vivo data. Furthermore, the computational complexity of the proposed beamforming model is four times less than that of the U-Net beamformer. The study results demonstrate that the proposed ultrasound image reconstruction method employing a deep learning beamformer, trained by the RF data from scatterers, can reconstruct a high-resolution image with a high frame rate for single plane-wave ultrasound imaging.

Improving Image Quality for Single-Angle Plane Wave Ultrasound Imaging With Convolutional Neural Network Beamformer.

Ultrafast ultrasound imaging based on plane wave (PW) compounding has been proposed for use in various clinical and preclinical applications, including shear wave imaging and super resolution blood flow imaging. Because the image quality afforded by PW imaging is highly dependent on the number of PW angles used for compounding, a tradeoff between image quality and frame rate occurs. In the present study, a convolutional neural network (CNN) beamformer based on a combination of the GoogLeNet and U-Net architectures was developed to replace the conventional delay-and-sum (DAS) algorithm to obtain high-quality images at a high frame rate. RF channel data are used as the inputs for the CNN beamformers. The outputs are in-phase and quadrature data. Simulations and phantom experiments revealed that the images predicted by the CNN beamformers had higher resolution and contrast than those predicted by conventional single-angle PW imaging with the DAS approach. In in vivo studies, the contrast-to-noise ratios (CNRs) of carotid artery images predicted by the CNN beamformers using three or five PWs as ground truths were approximately 12 dB in the transverse view, considerably higher than the CNR obtained using the DAS beamformer (3.9 dB). Most tissue speckle information was retained in the in vivo images produced by the CNN beamformers. In conclusion, only a single PW at 0° was fired, but the quality of the output image was proximal to that of an image generated using three or five PW angles. In other words, the quality-frame rate tradeoff of coherence compounding could be mitigated through the use of the proposed CNN for beamforming.