Sound Speed Estimation Research Articles

Spatial Ambiguity Correction in Coherence-Based Average Sound Speed Estimation.

Sound speed estimation can potentially correct the focusing errors in medical ultrasound. Maximizing the echo spatial coherence as a function of beamforming sound speed is a known technique to estimate the average sound speed. However, beamformation with changing sound speed causes a spatial shift of the echo signals resulting in noise and registration errors in the average sound speed estimates. We show that the spatial shift can be predicted and corrected, leading to superior sound speed estimates. Methods are presented for axial and 2-D location correction. Methods were evaluated using simulations and experimental phantom data. The location correction strategies improved the variance of sound speed estimates and reduced artifacts in the presence of strong backscatter variations. Limitations of the proposed methods and potential improvement strategies were evaluated.

Assessing Transducer Parameters for Accurate Medium Sound Speed Estimation and Image Reconstruction.

The influence of the transducer lens on image reconstruction is often overlooked. Lenses usually exhibit a lower sound speed than soft biological tissues. In academic research, the exact lens sound speed and thickness are typically unknown. Here, we present a simple and nondestructive method to characterize the lens sound speed and thickness as well as the time to peak of the round-trip ultrasound waveform, another key parameter for optimal image reconstruction. We applied our method to three transducers with center frequencies of 2.5, 7.5, and 15 MHz. We estimated the three parameters with an element-by-element transmission sequence that records internal reflections within the lens. We validated the retrieved parameters using an autofocusing approach that estimates sound speed in water. We show that the combination of our parameters estimation method with two-layer ray tracing outperforms standard image reconstruction. For all transducers, we successfully improved the accuracy of medium sound speed estimation, spatial resolution, and contrast. The proposed method is simple and robust and provides an accurate estimation of the transducer lens parameters and the time to peak of the ultrasound waveform, which leads to improved ultrasound image quality.

Real-Time Speed-of-Sound Estimation In Vivo via Steered Plane Wave Ultrasound.

Speed-of-sound (SoS) is an intrinsic acoustic property of human tissues and has been regarded as a potential biomarker of tissue health. To foster the clinical use of this emerging biomarker in medical diagnostics, it is important for SoS estimates to be derived and displayed in real time. Here, we demonstrate that concurrent global SoS estimation and B-mode imaging can be achieved live on a portable ultrasound scanner. Our innovation is hinged upon the design of a novel pulse-echo SoS estimation framework that is based on steered plane wave imaging. It has accounted for the effects of refraction and imaging depth when the medium SoS differs from the nominal value of 1540 m/s that is conventionally used in medical imaging. The accuracy of our SoS estimation framework was comparatively analyzed with through-transmit time-of-flight measurements in vitro on 15 custom agar phantoms with different SoS values (1508-1682 m/s) and in vivo on human calf muscles ( N = 9 ; SoS range: 1560-1586 m/s). Our SoS estimation framework has a mean signed difference (MSD) of - 0.6 ± 2.3 m/s in vitro and - 2.2 ± 11.2 m/s in vivo relative to the reference measurements. In addition, our real-time system prototype has yielded simultaneous SoS estimates and B-mode imaging at an average frame rate of 18.1 fps. Overall, by realizing real-time tissue SoS estimation with B-mode imaging, our innovation can foster the use of tissue SoS as a biomarker in medical ultrasound diagnostics.

Learning-based sound speed estimation and aberration correction for linear-array photoacoustic imaging

Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate for the SoS variations leads to aberration artefacts, deteriorating the image quality. Various methods have been proposed to address this issue, but they usually involve complex hardware and/or time-consuming algorithms, hindering clinical translation. In this work, we introduce a deep learning framework for SoS estimation and subsequent aberration correction in a dual-modal PA/US imaging system exploiting a clinical US probe. As the acquired PA and US images were inherently co-registered, the estimated SoS distribution from US channel data using a deep neural network was incorporated for accurate PA image reconstruction. The framework comprised an initial pre-training stage based on digital phantoms, which was further enhanced through transfer learning using physical phantom data and associated SoS maps obtained from measurements. This framework achieved a root mean square error of 10.2 m/s and 15.2 m/s for SoS estimation on digital and physical phantoms, respectively and structural similarity index measures of up to 0.88 for PA reconstructions compared to the conventional approach of 0.69. A maximum of 1.2 times improvement in the signal-to-noise ratio of PA images was further demonstrated with a human volunteer study. Our results show that the proposed framework could be valuable in various clinical and preclinical applications to enhance PA image reconstruction.

Bayesian estimation of dissipation and sound speed in tube measurements using a transfer-function model.

This study discusses acoustic dissipation, which contributes to inaccuracies in impedance tube measurements. To improve the accuracy of these measurements, this paper introduces a transfer function model that integrates diverse dissipation prediction models. Bayesian inference is used to estimate the important parameters included in these models, describing dissipation originating from various mechanisms, sound speed, and microphone positions. By using experimental measurements and considering a hypothetical air layer in front of a rigid termination as the material under test, Bayesian parameter estimation allows a substantial enhancement in characterization accuracy by incorporating the dissipation and sound speed estimates. This approach effectively minimizes residual absorption coefficients attributed to both boundary-layer effects and air medium relaxation. The incorporation of dissipation models leads to a substantial reduction (to 1%) in residual absorption coefficients. Moreover, the use of accurately estimated parameters further enhances the accuracy of actual tube measurements of materials using the two-microphone transfer function method.

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.)

Iterative Pulse-Echo Tomography for Ultrasonic Image Correction.

Acoustic aberration, caused by the uneven distribution of tissue speed-of-sound (SoS), significantly reduces the quality of ultrasound imaging. An important approach to mitigate this issue is imaging correction based on local SoS estimation. Computed ultrasound tomography in echo mode (CUTE) is an SoS estimation method that utilizes phase-shift information from ultrasound pulse-echo signals, offering both practical utility and computational efficiency. However, the traditional single-pass CUTE often suffers from poor accuracy and robustness. In this paper, an advanced approach known as iterative CUTE is introduced, which refines SoS estimates through iterative correction of errors and noise, addressing the limitations of traditional single-pass methods. It is argued that traditional precision indicators like root mean square error (RMSE) fall short in adequately reflecting the quality of SoS estimates for imaging correction, and coherence factor (CF) is proposed as a more indicative metric. Performance validation of the iterative CUTE algorithm was conducted using a simulation and agar phantom experiment. The results indicated that the iterative CUTE approach surpasses the single-pass approach, enhancing the average CF for SoS estimates by up to 18.2%. In phantom experiments, imaging corrected with SoS estimates from iterative CUTE reduced the Array Performance Index (API) by up to 40% compared to traditional methods.

Expendable Conductivity–Temperature–Depth-Assisted Fast Underwater Sound Speed Estimation by Convolutional Neural Network with Reduced Fully Connected Layers

Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimation methods cannot obtain real-time sound speed distribution or rely too much on sonar observation data, we propose an SSP estimation method based on a convolutional neural network with reduced fully connected layers (RFC-CNN) in this paper. This method utilizes neural networks to extract the complex nonlinear features of various types of data. With the help of the historical SSPs and shallow seawater sound speed and temperature data obtained by expendable conductivity–temperature–depth probes (XCTDs), a more accurate estimation of the regional sound speed distribution can be realized quickly. This approach can save the observation cost and significantly improve the real-time performance of SSP estimation.

Comparison of peripersonal space in front and rear spaces.

The space immediately around the body, referred to as the peripersonal space (PPS), plays a crucial role in interactions with external objects and in avoiding unsafe situations. This study aimed to investigate whether the size of the PPS changes depending on direction, with a particular focus on the disparity between the front and rear spaces. A vibrotactile stimulus was presented to measure PPS while a task-irrelevant auditory stimulus (probe) approached the participant. In addition, to evaluate the effect of the probe, a baseline condition was used in which only tactile stimuli were presented. The results showed that the auditory facilitation effect of the tactile stimulus was greater in the rear condition than in the front condition. Conversely, the performance on tasks related to auditory distance perception and sound speed estimation did not differ between the two directions, indicating that the difference in the auditory facilitation effect between directions cannot be explained by these factors. These findings indicate that the strength of audio-tactile integration is greater in the rear space compared to the front space, suggesting that the representation of the PPS differed between the front and rear spaces.

Transcranial Phase Correction Using Pulse-Echo Ultrasound and Deep Learning: A 2-D Numerical Study.

Phase aberration caused by human skulls severely degrades the quality of transcranial ultrasound images, posing a major challenge in the practical application of transcranial ultrasound techniques in adults. Aberration can be corrected if the skull profile (i.e., thickness distribution) and speed of sound (SOS) are known. However, accurately estimating the skull profile and SOS using ultrasound with a physics-based approach is challenging due to the complexity of the interaction between ultrasound and the skull. A deep learning approach is proposed herein to estimate the skull profile and SOS using ultrasound radiofrequency (RF) signals backscattered from the skull. A numerical study was performed to test the approach's feasibility. Realistic numerical skull models were constructed from computed tomography (CT) scans of five ex vivo human skulls in this numerical study. Acoustic simulations were performed on 3595 skull segments to generate array-based ultrasound backscattered signals. A deep learning model was developed and trained to estimate skull thickness and SOS from RF channel data. The trained model was shown to be highly accurate. The mean absolute error (MAE) was 0.15 mm (2% error) for thickness estimation and 13 m/s (0.5% error) for SOS estimation. The Pearson correlation coefficient between the estimated and ground-truth values was 0.99 for thickness and 0.95 for SOS. Aberration correction performed using deep-learning-estimated skull thickness and SOS values yielded significantly improved beam focusing (e.g., narrower beams) and transcranial imaging quality (e.g., improved spatial resolution and reduced artifacts) compared with no aberration correction. The results demonstrate the feasibility of the proposed approach for transcranial phase aberration correction.

Ultrasonic Sound Speed Estimation for Liver Fat Quantification: A Review by the AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative.

Non-alcoholic fatty liver disease (NAFLD) is a significant cause of diffuse liver disease, morbidity and mortality worldwide. Early and accurate diagnosis of NALFD is critical to identify patients at risk of disease progression. Liver biopsy is the current gold standard for diagnosis and prognosis. However, a non-invasive diagnostic tool is desired because of the high cost and risk of complications of tissue sampling. Medical ultrasound is a safe, inexpensive and widely available imaging tool for diagnosing NAFLD. Emerging sonographic tools to quantitatively estimate hepatic fat fraction, such as tissue sound speed estimation, are likely to improve diagnostic accuracy, precision and reproducibility compared with existing qualitative and semi-quantitative techniques. Various pulse-echo ultrasound speed of sound estimation methodologies have been investigated, and some have been recently commercialized. We review state-of-the-art in vivo speed of sound estimation techniques, including their advantages, limitations, technical sources of variability, biological confounders and existing commercial implementations. We report the expected range of hepatic speed of sound as a function of liver steatosis and fibrosis that may be encountered in clinical practice. Ongoing efforts seek to quantify sound speed measurement accuracy and precision to inform threshold development around meaningful differences in fat fraction and between sequential measurements.

Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data.

Ocean ambient noise field modelling and the optimized noise term

The objective of this thesis is to determine the frequency and wind-wave forcing dependent effective sea surface noise source level per unit area extracted from the hourly minimum sound power levels of six month-long acoustic recordings. The effect of the propagation environment is accounted for using Bellhop. The simulated environment is configured using climatological sound velocity profiles to capture seasonal effects and bottom sound speed estimates made from seabed sediment maps. Hourly meteorological data were extracted from ERA5 providing relevant wind and wave parameters from which noise levels may be predicted. A weighted composite model consisting of neutral wind and significant wave height leveraging the two-term exponential regression function proved to maximize model R2. Received level data originating from 16 hydrophone stations in the North Atlantic and Labrador Sea were combined with the Bellhop TL simulations in order to produce estimates of the effective noise source level per unit area (NSL/A) for changing surface environmental conditions and inter-compared. Hourly minimum sound power level derived model-data comparisons using horizontal wind speed magnitude 10 m above sea level expressed a decrease in NSL/A estimates versus Kewley (1990) by 10 to 15 dB from 1 to 3 kHz.

Refraction-Based Speed of Sound Estimation in Layered Media: an Angular Approach.

Speed of sound estimation in ultrasound imaging is a growing modality with several clinical applications such as hepatic steatosis stages quantification. A key challenge for clinically-relevant speed of sound estimation is to obtain repeatable values independent from superficial tissues and available in real-time. Recent works have demonstrated the feasibility to achieve quantitative estimations of the local speed of sound in layered media. However, such techniques require a high computational power and exhibit instabilities. We present a novel speed of sound estimation technique based on an angular approach of ultrasound imaging in which plane-waves are considered in transmit and in receive. This change of paradigm allows us to rely on refraction properties of plane-waves to infer local speed of sound values directly from the angular raw-data. The proposed method robustly estimates the local speed of sound with only few ultrasound emissions and with a low computational complexity which makes it compatible with real-time imaging. Simulations and in vitro experimental results show that the proposed method outperforms state-of-the-art approaches with biases and standard deviations lower than 10ms-1, 8 times less emissions and 1000 times lower computational time. Further in vivo experiments validate its performance for liver imaging.

Electron–Ion Temperature Ratio in Astrophysical Shocks

Collisionless shock waves in supernova remnants and the solar wind heat electrons less effectively than they heat ions, as is predicted by kinetic simulations. However, the values of T e /T p inferred from the Hα profiles of supernova remnant shocks behave differently as a function of Mach number or Alfvén Mach number than what is measured in the solar wind or predicted by simulations. Here we determine T e /T p for supernova remnant shocks using Hα profiles, shock speeds from proper motions, and electron temperatures from X-ray spectra. We also improve the estimates of sound speed and Alfvén speed used to determine Mach numbers. We find that the Hα determinations are robust and that the discrepancies among supernova remnant shocks, solar wind shocks, and computer-simulated shocks remain. We discuss some possible contributing factors, including shock precursors, turbulence, and varying preshock conditions.

Ex vivo evaluation of the sensitivity of modified average speed of sound estimation method

The speed of sound (SoS), which is the propagation speed of longitudinal waves, is expected to be effective for the early detection and monitoring of diseases. Several methods that use handheld probes have been proposed to estimate the SoS. In this study, a modified average SoS estimation method, referred to as the improved focusing method, is proposed to increase the accuracy of average SoS estimation. In the proposed method, a plane wave was transmitted, and the center-element position-dependent variations in the SoS estimation were reduced. Ex vivo experiments were conducted using chicken liver to evaluate the applicability and sensitivity of the proposed method to actual tissues. The livers were subjected to continuous temperature changes to induce changes in the SoS. The results suggest that the improved focusing method measures the average SoS accurately as well as small SoS changes of approximately 1.5 m·s−1·°C−1.

Speed-of-sound estimation in ultrasound propagation medium by considering size of target scatterer.

Accurate speed-of-sound (SoS) estimation in an ultrasound propagation medium improves imaging quality and contributes to better diagnosis of diseases. In conventional time-delay-based SoS estimation approaches studied by several groups, a received wave is assumed to be scattered from an ideal point scatterer. In these approaches, the SoS is overestimated when the target scatterer has a non-negligible size. In this paper, we propose the SoS estimation method that considers target size. In the proposed method, the error ratio of the estimated SoS using the conventional time-delay-based approach is determined from measurable parameters using the geometric relationship between the received elements and target. Subsequently, the SoS erroneously estimated using conventional estimation, assuming the ideal point scatterer as a target, is corrected by the determined estimation error ratio. To validate the proposed method, the SoS in water was estimated for several wire sizes. The SoS in the water was overestimated using the conventional SoS estimation method, with a maximum positive error of 38m/s. The proposed method corrected the SoS estimates, and the errors were suppressed to within 6m/s, irrespective of the wire diameter. The present results demonstrate that the proposed method can estimate the SoS by considering the target size without using information on the true SoS, true target depth, and true target size, which is applicable to in vivo measurements.

Computationally efficient deep learning-based image reconstruction for sound speed estimation in ultrasound computed tomography

Ultrasound computed tomography (USCT) has great potential for breast cancer imaging due to its ability to quantify acoustic tissue properties such as the speed of sound (SOS). Full-waveform inversion (FWI)-based image reconstruction methods can produce high spatial resolution SOS images, but these methods are computationally demanding, especially when formulated in the time-domain. Deep learning (DL)-based methods can be computationally efficient and provide high-quality object estimates as studied in various medical image reconstruction problems. However, there remains a further need to develop and assess the DL-based image reconstruction methods for USCT for use in medical imaging applications. Here, we present a supervised DL approach for USCT image reconstruction that is formulated as an image-to-image translation problem. Specifically, a low-resolution SOS image obtained via traveltime tomography and a high-resolution reflection tomography image are used as inputs to a U-Net, which is trained to estimate a high-resolution SOS map. These input images are complementary, providing both quantitative information about SOS values and information about tissue boundaries. Numerical studies that employ realistic numerical breast phantoms demonstrated that the proposed method produced high-quality SOS estimates based on image quality metrics such as NMSE, SSIM, and PSNR.

Wide-area sound speed profile estimation based on a pre-classification scheme for sound speed perturbation modes

IntroductionThe trend of sound speed profile (SSP) inversion is towards wide-area sound speed estimation. However, the traditional inversion method of dividing the latitude and longitude grids has limitations in terms of significantly lower accuracy when samples are lacking. k-means clustering algorithm (K-means) can divide the training class to achieve high accuracy estimation.MethodThis paper proposes a grid-free pre-classification inversion scheme based on empirical orthogonal function (EOF) vectors. The scheme is based on the K-means to classify the samples according to the perturbation mode of the SSP. After classification, the SSP inversion is carried out using the self-organizing map algorithm (SOM). The experimental sea area is selected from the South China Sea, and the inversion results are evaluated using root mean square error (RMSE) as the criterion.ResultThe inversion results show that the inversion error is 2.1 m/s for the pre-classification solution and 2.7 m/s for the solution without pre-classification, a steady improvement of more than 20% in the inversion error. Accuracy is also improved by 2.14 m/s in the depth range where the sound speed perturbance is greatest.DiscussionThis pre-classification scheme has smaller inversion errors and the classification results are reasonable in terms of distribution in time and space. It provides a feasible solution for SSP inversion in sea areas where samples are lacking.

Probing the pressure dependence of sound speed and attenuation in bubbly media: Experimental observations, a theoretical model and numerical calculations

The problem of attenuation and sound speed of bubbly media has remained partially unsolved. Comprehensive data regarding pressure-dependent changes of the attenuation and sound speed of a bubbly medium are not available. Our theoretical understanding of the problem is limited to linear or semi-linear theoretical models, which are not accurate in the regime of large amplitude bubble oscillations. Here, by controlling the size of the lipid coated bubbles (mean diameter of ≈5.4μm), we report the first time observation and characterization of the simultaneous pressure dependence of sound speed and attenuation in bubbly water below, at and above microbubbles resonance (frequency range between 1–3 MHz). With increasing acoustic pressure (between 12.5–100 kPa), the frequency of the peak attenuation and sound speed decreases while maximum and minimum amplitudes of the sound speed increase. We propose a nonlinear model for the estimation of the pressure dependent sound speed and attenuation with good agreement with the experiments. The model calculations are validated by comparing with the linear and semi-linear models predictions. One of the major challenges of the previously developed models is the significant overestimation of the attenuation at the bubble resonance at higher void fractions (e.g. 0.005). We addressed this problem by incorporating bubble–bubble interactions and comparing the results to experiments. Influence of the bubble–bubble interactions increases with increasing pressure. Within the examined exposure parameters, we numerically show that, even for low void fractions (e.g. 5.1×10-6) with increasing pressure the sound speed may become 4 times higher than the sound speed in the non-bubbly medium.