Single Plane Wave Research Articles

Image quality improvement in single plane-wave imaging using deep learning

In ultrasound image diagnosis, single plane-wave imaging (SPWI), which can acquire ultrasound images at more than 1000 fps, has been used to observe detailed tissue and evaluate blood flow. SPWI achieves high temporal resolution by sacrificing the spatial resolution and contrast of ultrasound images. To improve spatial resolution and contrast in SPWI, coherent plane-wave compounding (CPWC) is used to obtain high-quality ultrasound images, i.e., compound images, by coherent addition of radio frequency (RF) signals acquired by transmitting plane waves in different directions. Although CPWC produces high-quality ultrasound images, their temporal resolution is lower than that of SPWI. To address this problem, some methods have been proposed to reconstruct a ultrasound image comparable to a compound image from RF signals obtained by transmitting a small number of plane waves in different directions. These methods do not fully consider the properties of RF signals, resulting in lower image quality compared to a compound image. In this paper, we propose methods to reconstruct high-quality ultrasound images in SPWI by considering the characteristics of RF signal of a single plane wave to obtain ultrasound images with image quality comparable to CPWC. The proposed methods employ encoder–decoder models of 1D U-Net, 2D U-Net, and their combination to generate the high-quality ultrasound images by minimizing the loss that considers the point spread effect of plane waves and frequency spectrum of RF signals in training. We also create a public large-scale SPWI/CPWC dataset for developing and evaluating deep-learning methods. Through a set of experiments using the public dataset and our dataset, we demonstrate that the proposed methods can reconstruct higher-quality ultrasound images from RF signals in SPWI than conventional method.

Interception force assisted optical pulling of a dipole nanoparticle in a single plane wave

The optical pulling force is generally believed to originate from the recoil force due to the simultaneous excitation of multipoles in the particle, which overcomes the interception force contributing to the optical pushing force. However, we show that the interception force can induce optical pulling force on a small isotropic spherical particle with gain in a uniform electromagnetic plane wave, in which multipole excitation is negligible within the dipole regime. Based on the multipole expansion theory, a rigorous analytical expression is derived for optical force acting on a spherical particle of arbitrary size and composition illuminated by a single plane wave, regardless of its polarization. The analytical results show that the interception force, which is typically positive in a conventional dielectric particle under illumination of a single plane wave, undergoes a crossover from positive to negative by introducing appropriate gain into the dipolar dielectric nanoparticle, thereby giving rise to the optical pulling. It’s deserved to be noted that the optical pulling force assisted by the interception force does not weaken in magnitude, in fact, it exhibits a stronger magnitude compared to the optical pushing force experienced by a corresponding conventional dielectric particle.

Complex Residual Attention U-Net for Fast Ultrasound Imaging from a Single Plane-Wave Equivalent to Diverging Wave Imaging.

Plane wave imaging persists as a focal point of research due to its high frame rate and low complexity. However, in spite of these advantages, its performance can be compromised by several factors such as noise, speckle, and artifacts that affect the image quality and resolution. In this paper, we propose an attention-based complex convolutional residual U-Net to reconstruct improved in-phase/quadrature complex data from a single insonification acquisition that matches diverging wave imaging. Our approach introduces an attention mechanism to the complex domain in conjunction with complex convolution to incorporate phase information and improve the image quality matching images obtained using coherent compounding imaging. To validate the effectiveness of this method, we trained our network on a simulated phased array dataset and evaluated it using in vitro and in vivo data. The experimental results show that our approach improved the ultrasound image quality by focusing the network's attention on critical aspects of the complex data to identify and separate different regions of interest from background noise.

On the phase difference of ECH waves obtained from the interferometry observation by the Arase satellite

We analyzed electrostatic electron cyclotron harmonic waves observed by the interferometry observation mode of the Arase satellite. It is found that the magnitude of the phase difference varies with the satellite spin. The spin dependence of this phase difference was investigated by examining the trend of the spin dependence for the 84 events of interferometry observation of ECH waves. We found that they are divided into two categories. One is that the phase difference tends to show sinusoidal variations as a function of the angle γB\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma _B$$\\end{document} between the ambient magnetic field projected on the spin plane and the electric field sensor. The other is that the phase difference is close to zero and does not depend on γB\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma _B$$\\end{document}. A numerical model of interferometry observation of single plane wave is constructed to explain the observed phase differences. We performed the numerical calculations when the background magnetic field was oriented in the direction often observed in the Arase satellite. The result of the calculations shows the wave vector direction relates to the spin angle with the maximum phase difference. Using this relation, we show that it may be possible to estimate the wave vector direction of ECH waves from one-dimensional interferometry data. This is expected to enable more accurate estimates of phase velocity.Graphical

Deep coherence learning: An unsupervised deep beamformer for high quality single plane wave imaging in medical ultrasound

Plane wave imaging (PWI) in medical ultrasound is becoming an important reconstruction method with high frame rates and new clinical applications. Recently, single PWI based on deep learning (DL) has been studied to overcome lowered frame rates of traditional PWI with multiple PW transmissions. However, due to the lack of appropriate ground truth images, DL-based PWI still remains challenging for performance improvements. To address this issue, in this paper, we propose a new unsupervised learning approach, i.e., deep coherence learning (DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the DL network is trained to predict highly correlated signals with a unique loss function from a set of PW data, and the trained DL model encourages high-quality PWI from low-quality single PW data. In addition, the DL-DCL framework based on complex baseband signals enables a universal beamformer. To assess the performance of DL-DCL, simulation, phantom and in vivo studies were conducted with public datasets, and it was compared with traditional beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based methods (i.e., supervised learning approach with 1-PW and generative adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial resolution, and it outperformed all comparison methods in contrast resolution. These results demonstrated that the proposed unsupervised learning approach can address the inherent limitations of traditional PWIs based on DL, and it also showed great potential in clinical settings with minimal artifacts.

Simulation of femtosecond laser-induced periodic surface structures on fused silica by considering intrapulse and interpulse feedback

The formation of laser-induced periodic surface structures (LIPSSs) on fused silica upon irradiation with plane wave, double pulse, spot processing, and scanning processing (pulse duration tp = 35 fs, center wavelength λ = 800 nm, low repetition rate ≈1 kHz) is studied theoretically with an improved three-dimensional finite-difference time-domain plasma model. The model covers both intrapulse feedback under single shot and interpulse feedback under multi-shots, thus enabling better prediction of transient responses during laser–material interaction and the evolution of the ablated morphology and accumulated defects’ density with more shots. In simulations of a single plane wave, a double pulse can modulate LIPSS periodicity. In simulations of spot processing with Gaussian beam, an increase in the number of shots results in a noticeable ablation pattern where high-spatial-frequency LIPSS surrounds low-spatial-frequency LIPSS at a fluence of 2.8 J/cm2. Moreover, simulations of scanning processing with Gaussian beam showcase the broad applicability of this model, revealing that the orientation of the LIPSS depends on the polarization direction rather than the scanning path. This new model provides a powerful tool to simulate the formation of LIPSS on silica, particularly when temporally modulated laser is involved or predicting the evolution of morphology dependent on the number of shots.

Simultaneous Coherent and Displacement Compounding for 2-D Noninvasive Carotid Strain Imaging: A Proof of Principle Study.

Arteriosclerosis results from lipid buildup in artery walls, leading to plaque formation, and is a leading cause of death. Plaque rupture can cause blood clots that might lead to a stroke. Distinguishing plaque types is a challenge, but ultrasound (US) elastography can help by assessing plaque composition based on strain values. Since the artery has a circular structure, an accurate axial and lateral displacement strategy is needed to derive the radial and circumferential strains. A high-precision lateral displacement is challenging due to the lack of phase information in the lateral direction of the beamformed RF data. Previously, our group has developed a compounding technique in which the lateral displacement is estimated using triangulation of the axial displacement estimated from transmitting and beamforming US beams at ±20°. However, transmitting with a single plane wave will reduce signal-to-noise and contrast-to-noise ratio as well as lateral resolution. In this article, we combine our displacement compounding with coherent compounding. Instead of transmitting a single plane wave, multiple plane waves are transmitted at certain angles centered on the angle of the beamforming grids, and then, the backscattered wavefronts are beamformed and coherently compounded on the center of the transmit beams (-20°, +20°, and 0°). The numerical investigation using the GE9LD probe ( f0 = 5.32 MHz, pitch = 230 μ m, and width = 43.9 mm) led us to 19 plane waves spanning angles within -10° to 10° (with respect to center of the transmit beams), resulting in a total of 57 plane wave transmit (for three beamforming grids at 0° and ±20°). FIELD II simulations of a cylindrically shaped phantom (mimicking the carotid artery) at a signal-to-noise ratio (SNR) ≥ 20 dB show that the proposed method decreases the root-mean-square error (RMSE) of the lateral displacement and strain estimations by 40% and 45% compared to the previous method, respectively. The results of our experiments with a carotid artery phantom [made out of 10% polyvinyl alcohol (PVA)] show that the proposed method provides strain images with higher quality and more in agreement with the theory, with 26% lower standard deviation, especially at the peak systolic phase. The proposed method paves the path toward improved quality in vivo 2-D strain imaging using our displacement compounding technique and translating it to 3-D with a row-column array.

Ultrasound channel attention residual network for medical plane wave echo data-based average sound speed estimation

The average sound speed estimation is crucial for ultrasound imaging quality and diagnostic. In this article, the deep learning techniques were utilized and an innovative Ultrasound Channel Attention Residual Network (UCA-ResNet) was proposed. The UCA-ResNet incorporated a specially designed Ultrasound Channel Attention (UCA) block, which effectively enhanced relevant ultrasound channel features and convolutional channel features. For evaluation, the simulation, phantom, and in vivo experiments were conducted. In the simulation experiment, UCA-ResNet achieved remarkable results, with a mean absolute error (MAE) of 0.40 m/s, root mean square error (RMSE) of 1.25 m/s, standard deviation of error (SDE) of 1.25 m/s, and a one-time estimation time of 3.67 ms. Moreover, the phantom and in vivo experiments further validated the high accuracy and low computational cost of UCA-ResNet. The UCA-ResNet can accurately estimate the average sound speed using a single plane wave echo data while maintaining low computational cost. It has potential in enhancing medical ultrasound imaging quality and providing novel diagnostic insights. (Code and data will be available upon acceptance of the manuscript.)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning.

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been mostly trained on simulated or phantom images, leading to suboptimal performance on in vivo images. In this study, we present a method to enhance the quality of single plane wave (PW) acquisitions using a CNN trained on in vivo images. Our contribution is twofold. Firstly, we introduce a training loss function that accounts for the high dynamic range of the radio frequency data and uses the Kullback-Leibler divergence to preserve the probability distributions of the echogenicity values. Secondly, we conduct an extensive performance analysis on a large new in vivo dataset of 20,000 images, comparing the predicted images to the target images resulting from the coherent compounding of 87 PWs. Applying a volunteer-based dataset split, the peak signal-to-noise ratio and structural similarity index measure increase, respectively, from 16.466 ± 0.801 dB and 0.105 ± 0.060, calculated between the single PW and target images, to 20.292 ± 0.307 dB and 0.272 ± 0.040, between predicted and target images. Our results demonstrate significant improvements in image quality, effectively reducing artifacts.

Unique Determination of a Planar Screen in Electromagnetic Inverse Scattering

The target of our research is the object being a highly conducting thin plate or a flat screen. We especially focus on the question of when a single measurement uniquely determines an object. By this, we mean that we have one fixed transmitted wave and the resulting scattered field is measured for all directions in the far field. Such measurements are called passive, since there is no need to move the transmitter after its position has been fixed. We show that the far field of a scattered electromagnetic field corresponding to a single incoming plane wave always uniquely determines a bounded super-conductive planar screen. This generalises a previous acoustic result.

Uniqueness in determining rectangular grating profiles with a single incoming wave (part II): TM polarization case

This paper is concerned with an inverse transmission problem for recovering the shape of a penetrable rectangular grating sitting on a perfectly conducting plate. We consider a general transmission problem with the coefficient λ ≠ 1 which covers the transverse magnetic (TM) polarization case. It is proved that a rectangular grating profile can be uniquely determined by the near-field observation data incited by a single plane wave and measured on a line segment above the grating. In comparison with the transverse electric (TE) case (λ = 1), the wave field cannot lie in H 2 around each corner point, bringing essential difficulties in proving uniqueness with one plane wave. Our approach relies on singularity analysis for Helmholtz transmission problems in a right-corner domain and also provides an alternative idea for treating the TE transmission conditions which were considered in the authors’ previous work (Xiang and Hu 2023 Inverse Problems 39 055004).

The Effect of Spatial Velocity Gradients on Block-Matching Accuracy for Ultrasound Velocimetry

Block matching serves as the foundation for ultrasound velocimetry techniques such as blood speckle tracking and echo-particle image velocimetry. Any spatial velocity gradients (SVGs) inside a block-matching pair will result in tracking error, due to both the finite block size and the ultrasound point-spread-function. We assess, using an in silico sinusoidal flow phantom, the effect of SVG magnitude and beam-to-flow angle on block-matching bias and precision. Secondarily we assess the effect that SVGs have on velocimetry bias when using angled plane-wave compounding. The magnitude and angle of SVGs were varied by adjusting the wavelength and direction of a sinusoidal flow profile. Scatterers displaced by this flow profile were used for simulating ultrasound radio frequency data at discrete time points. After beamforming, the 2-D flow field was estimated using block matching. Two imaging sequences were tested, a single plane-wave and a three-angled plane-wave. Smaller sinusoidal flow wavelengths resulted in increased bias and reduced precision, revealing an inverse relationship between sinusoidal flow wavelength and tracking error, with median errors ranging from 69%-90% for the smallest flow wavelengths (highest SVGs) down to 3%-5% for the largest (lowest SVGs). The SVG angle was also important, in which lateral SVGs (with axially oriented flows) resulted in significant speckle decorrelation and high tracking errors in regions with high SVGs. Conversely, axial SVGs (laterally oriented flow) experienced higher bias in the peak velocity regions of the flow profile. Coherent compounding resulted in higher velocity errors than using a single transmission for lateral SVGs but not for axial SVGs. The highest SVGs that could be measured with ≤10% error was when the sinusoidal flow wavelength was less than 20 times the ultrasound pulse wavelength. The clinical significance is that the high SVGs present in high kinetic energy flows, such as severe carotid stenosis and aortic regurgitation, will limit the ability to accurately quantify the velocities in these flow structures.

Accurate analysis of bistatic scattering problems using compressive sensing with a new sparse basis

Compressive sensing (CS) used as a sparse basis can enhance the characteristic basis functions (CBFs), which helps in analyzing the bistatic scattering problems of a three-dimensional (3D) object. However, a sparse basis constructed by the CBFs lacks current information under a single plane wave, leading to a low computation accuracy when analyzing complex objects. To resolve this issue, a new sparse basis construction method is presented in this work, which considers the current information under multilevel plane waves with different incident angles and different polarization modes. As this new sparse basis contains more comprehensive current information, the accuracy is considerably improved when analyzing the complex targets. Furthermore, the accuracy and efficiency of the proposed method are verified by extensive numerical simulations.

An improved Fourier Ptychography algorithm for ultrasonic array imaging

Inspired by the optical imaging algorithm, the Fourier Ptychography (FP) algorithm is adopted to improve the resolution of ultrasonic array imaging. In the FP algorithm, the steady-state spectrum is utilized to recover the high-resolution ultrasonic images. Meanwhile, the parameters of FP algorithm are empirical, which can affect the imaging quality of ultrasonic array. Then the particle swarm optimization (PSO) algorithm is used to optimize the parameters of FP algorithm to further improve the imaging quality of ultrasonic array. The tungsten imaging experiments and pig eye imaging experiments are conducted to demonstrate the feasibility and effectiveness of the developed algorithm. In addition, the proposed algorithm and the coherent wave superposition (CWS) algorithm are both based on single plane wave (SPW) algorithms and they are then compared. The results show that the CWS algorithm and FP algorithm have good longitudinal and lateral resolutions, respectively. The particle swarm optimization-based FP (PSOFP) imaging algorithm has both excellent lateral and longitudinal resolutions. The average lateral resolution of PSOFP imaging algorithm is improved by 34.47% compared with CWS imaging algorithm in the tungsten wires experiments, and the lateral boundary structure width of the lens is improved by 49.48% in the pig eye experiments. The proposed algorithm can effectively improve the ultrasonic imaging quality for medical application.

Ultrasonic imaging using conditional generative adversarial networks

The Full Matrix Capture (FMC) and Total Focusing Method (TFM) combination is often considered the gold standard in ultrasonic nondestructive testing, however it may be impractical due to the amount of time required to gather and process the FMC, particularly for high cadence inspections. This study proposes replacing conventional FMC acquisition and TFM processing with a single zero-degree plane wave (PW) insonification and a conditional Generative Adversarial Network (cGAN) trained to produce TFM-like images. Three models with different cGAN architectures and loss formulations were tested in different scenarios. Their performances were compared with conventional TFM computed from FMC. The proposed cGANs were able to recreate TFM-like images with the same resolution while improving the contrast in more than 94% of the reconstructions in comparison with conventional TFM reconstructions. Indeed, thanks to the use of a bias in the cGANs’ training, the contrast was systematically increased through a reduction of the background noise level and the elimination of some artifacts. Finally, the proposed method led to a reduction of the computation time and file size by a factor of 120 and 75, respectively.

Uniqueness in determining rectangular grating profiles with a single incoming wave (Part I): TE polarization case

We investigate inverse diffraction problems for penetrable gratings in a piecewise constant medium. In the TE polarization case, it is proved that a rectangular grating profile together with the refractive index beneath it can be uniquely determined by the near-field observation data incited by a single plane wave and measured on a line segment above the grating. Our approach relies on the expansion of solutions to the Helmholtz equation and the corner singularity analysis of solutions to the inhomogeneous Laplace equation with a piecewise continuous source term in a sector. This paper also contributes to corner scattering theory for the Helmholtz equation in a special non-convex domain.

A robust cascaded deep neural network for image reconstruction of single plane wave ultrasound RF data

Reconstruction of ultrasound data from single plane wave Radio Frequency (RF) data is a challenging task. The traditional Delay and Sum (DAS) method produces an image with low resolution and contrast, if employed with RF data from only a single plane wave. A Coherent Compounding (CC) method that reconstructs the image by coherently summing the individual DAS images was proposed to enhance the image quality. However, CC relies on a large number of plane waves to accurately sum the individual DAS images, hence it produces high quality images but with low frame rate that may not be suitable for time-demanding applications. Therefore, there is a need for a method that can create a high quality image with higher frame rates. Furthermore, the method needs to be robust against the input transmission angle of the plane wave. To reduce the method’s dependence on the input angle, we propose to unify the RF data at different angles by learning a linear data transformation from different angled data to a common, 0° data. We further propose a cascade of two independent neural networks to reconstruct an image, similar in quality to CC, by making use of a single plane wave. The first network, denoted as “PixelNet”, is a fully Convolutional Neural Network (CNN) which takes in the transformed time-delayed RF data as input. PixelNet learns optimal pixel weights that get element-wise multiplied with the single angle DAS image. The second network is a conditional Generative Adversarial Network (cGAN) which is used to further enhance the image quality. Our networks were trained on the publicly available PICMUS and CPWC datasets and evaluated on a completely separate, CUBDL dataset obtained from different acquisition settings than the training dataset. The results thus obtained on the testing dataset, demonstrate the networks’ ability to generalize well on unseen data, with frame rates better than the CC method. This paves the way for applications that require high-quality images reconstructed at higher frame rates.

Investigation on propagation mechanism of leakage acoustic waves in gas-liquid stratified flow

Propagation speed of acoustic waves is of vital importance in pipeline leakage detection using acoustic method. However, the wave front shape and thermodynamic process of leakage acoustic waves in gas-liquid stratified flow remain unclear, which restricts the proper calculation of sound speed in this flow pattern. This paper presents a simulation study using CFD software Fluent to investigate the propagation process of leakage acoustic waves in stratified flow. The leakage acoustic waves are generated in different fluid phases and small time-step simulation is implemented to study the pressure behavior during the wave propagation. The results show that leakage acoustic waves propagate as single plane wave in gas-liquid stratified flow and the propagation process is nearly adiabatic. The value of polytropic exponent when calculating the sound speed in gas phase should be 1.4. Further, experimental study is conducted to verify the simulation results. Influencing factors including pressure and void fraction are considered. The present results contribute to the proper selection of sound speed calculation model in stratified flow, which can improve the leakage detection accuracy in gas-liquid stratified flow pipelines.

Scattering properties of acoustic beams off spinning objects: Induced radiation force and torque

Understanding the acoustic scattering and radiation force and torque of an object is important in various fields, such as underwater communication, acoustic imaging, and noninvasive characterization, as well as biomedical ultrasound. Generally, acoustic scattering is considered for static (non-moving) objects and the impinging signal is typically a plane wave. Here, we consider scattering off cylindrical objects in spinning motion around the axis of rotational symmetry. We investigate the radiation force and torque induced by various incident signals, e.g., cylindrical diverging and converging beams as well as quasi-Gaussian beams of different orders. It is assumed in this study (unless otherwise stated) that the acoustic parameters of the objects (density and compressibility) are identical to those of the surrounding medium, in order to isolate the effects purely attributed to rotation. The scenario of a spinning inhomogeneous object is also shown to play a prominent role for generating torque with single plane waves. Our findings may add to the current interest in time-varying and moving metamaterials and open vistas in manipulation of movement and position of ultra-small objects via acoustic beams.

Optimization of Acoustic Channels to Minimize Scattered Pressure Fields

Acoustic and elastic metamaterials have shown potential for many different applications in wave manipulation and structural isolation. Minimization of a scattered wave field has been identified as an important use for such materials, and a wide variety of designs have been proposed for this purpose. Several of these designs face practical challenges (e.g. pentamode designs, which are difficult to manufacture), and many others involve numerous tunable parameters and a large design parameter space (e.g. distributions of resonators), making global search-based optimization less tractable and raising the need for gradient-based methods. In this work, gradient-based optimization governed by partial differential equations (PDEs) is used to optimize acoustic channels in a coiled-space metastructure layer surrounding a rigid acoustic scatterer. The optimization technique is chosen to more feasibly search the design parameter space, and the acoustic channel design is chosen to exploit sound speed optimization as a proxy for channel length optimization to give practically achievable structures. Results are presented for both a single plane wave direction and multiple plane wave directions, as well as for both a single frequency and multiple frequencies. The equivalence of sound speed optimization and channel length optimization is demonstrated for a feasible design minimizing the scattered pressure field. Designs with various numbers of optimized channels are also considered.