Wave Imaging Research Articles

Mechanical wave velocities by clutter filter wave imaging detects myocardial dysfunction in acute coronary syndrome

Abstract Background/introduction Mechanical waves within the myocardium arising from physiologic events in the cardiac cycle such as the atrial kick (AK), mitral valve closure (MVC) and aortic valve closure (AVC) offer insight to tissue stiffness and can be measured by high frame rate echocardiography. Purpose To investigate the feasibility of measuring mechanical wave velocities in the left ventricle by clutter filter wave imaging (CFWI) and its potential for detecting regional myocardial dysfunction in patients with acute coronary syndrome. Methods We examined 60 patients (66±9 years of age, 72% male) with acute coronary syndrome by high frame rate echocardiography after revascularisation, imaging the left ventricle from the parasternal long axis view and three apical projections. Intrinsic mechanical waves generated by the AK, MVC and AVC were analysed off-line by CFWI of all walls using custom software (PyMWI) on 2000 frames of in-phase and quadrature component data per view extracted from a Vivid E95 scanner. Results We analysed a total number of 2372 waves recorded with a frame rate of 1180 (1134-1199) frames per second. The feasibility for measuring AK waves was high in all regions (mean 89%), but lower for the MVC and AVC waves with significant variability between regions (mean 44% and 46%, respectively; p<0.001). Feasibility declined from basal to apical segments for all waves (p=0.0001). Mean mechanical wave velocity was slowest for the AK wave at 2.5 (2.0-3.0) m/s, faster for the AVC wave at 5.1 (3.5-6.3) m/s and fastest for the MVC wave at 6.6 (4.9-8.6) m/s (p=0.0001). Basal AK wave velocity was faster in patients with basal wall motion abnormalities compared to those without (2.7 vs. 2.0 m/s, p=0.0004) and could detect the presence of wall motion abnormalities adjusting for age, gender and parameters of diastolic function (univariable OR 3.4, 95% CI 1.5-7.7, p<0.01; multivariable OR 4.4, 95% CI 1.4-14.1, p=0.01). Basal AK wave velocity detected wall motion abnormalities with an AUC of 0.78 with cut-off speeds of 2.3 m/s and 3.0 m/s, both correctly classifying 73% of cases (sensitivity/specificity 81%/68% and 43%/89%, respectively). Mechanical wave velocities of other waves and in other regions were not consistently and only weakly associated with echocardiographic parameters of systolic and diastolic function. Conclusion(s) Mechanical wave velocities by CFWI detects myocardial dysfunction in acute coronary syndrome. Elevated basal AK wave velocity was independently associated with myocardial dysfunction where AK wave velocity ≥2.3 m/s provided a good sensitivity and ≥3.0 m/s a good specificity for detecting regional wall motion abnormalities. The feasibility of measuring the AK wave was good, whereas MVC and AVC waves had lower feasibility and were inadequate for assessment of myocardial function. Feasibility variations between AK and MVC/AVC waves suggest differences in wave propagation dynamics or intrinsic wave properties.Illustration Clutter Filter Wave ImagingRisk of myocardial dysfunction

Shear wave speed, dispersion slope and anisotropy evaluation using cardiac shear wave elastography in patients with Fabry disease

Abstract Background Cardiac shear wave imaging has been proposed to investigate left ventricular myocardial alterations non-invasively. Dispersion slope and anisotropy are fundamental properties that have not been studied in Fabry disease (FD) patients yet. Purpose The aim of this study was to determine and compare shear wave propagation speed, dispersion slope and fractional anisotropy in FD patients and healthy controls. Methods We prospectively included 20 FD patients and 20 healthy controls (HC). Shear wave speed, dispersion slope and fractional anisotropy were measured non-invasively in anteroseptal basal segment using an ultrasound for cardiac shear wave imaging. Echocardiography, electrocardiogram and laboratory evaluations were performed. Results Shear wave propagation speed was higher in FD patients than in HC group (1.46 ± 0.12 m/s and 1.36 ± 0.17 m/s, p = 0.03). No difference was found in dispersion slope (11.4 m/s/kHz in FD group and 11.3 m/s/kHz in HC group, p = 0.6). Fractional anisotropy was 0.05 ± 0.04 in FD group and 0.05 ± 0.04 in HC group, p = 0.4. Conclusion Shear wave speed was higher in FD patients compared to control group. Dispersion slope and fractional anisotropy in FD group did not reveal significant difference to the control group.

Early detection of cardiac dysfunction using shear wave imaging: insights into reduced cardiorespiratory fitness in childhood cancer survivors

Abstract Background Diastolic dysfunction is thought to be the earliest cardiotoxic sequel after cancer therapy among childhood cancer survivors. Despite many survivors do exhibit reduced cardiorespiratory fitness (CRF) potentially due to cardiotoxicity, current echocardiographic parameters fail to detect hidden diastolic dysfunction early after therapy (1). High frame rate (HFR) shear wave imaging (SWI) is a novel tool that could give insights about myocardial stiffness, which is a key determining factor of diastolic function. Purpose We aimed at testing the ability of SWI to detect early alterations in myocardial stiffness among childhood cancer survivors and its relation with the reduced CRF. Methods Fifty childhood cancer survivors (mean 20 ±3 years) who received chemo-and/or- radiotherapy (mean time after treatment: 4 ±3 years) and thirty-two matched healthy volunteers (HV) were recruited. Conventional as well as HFR echocardiographic images (ca. 1200 frames/second) were acquired at rest using a modified commercial echocardiography machine. All participants underwent an incremental, symptom-limited cardiopulmonary exercise test on a treadmill measuring peak oxygen consumption (VO2 peak). HFR data were analysed offline. An anatomical M mode was drawn along the ventricular septum, and tissue Doppler acceleration maps were extracted to measure SW velocities after mitral valve closure (MVC) (Figure 1). Results Among cancer survivors, left ventricular ejection fraction (LVEF) and global longitudinal strain were significantly lower compared to HV (56 % ±4 vs 59 % ±4, and 17.6 % ±1.6 vs 19.1 % ±1.2, respectively; p <0.001). However, both LVEF and GLS were within the normal range and none of them correlated with the peak VO2 (Figure 2A&B). None of the echocardiographic diastolic parameters (e.g. mitral E, E/e’, left atrial volume or TR jet velocity) showed significant difference between both groups (P > 0.05). The mean SW velocities were significantly higher in cancer survivors compared to HV (3.9 ±0.6 m/s vs 3.2 ±0.3 m/s respectively; p <0.001). Interestingly, SW velocities among cancer survivors showed moderate significant negative correlation with the VO2 peak (r= -0.590, p <0.001) indicating increasing myocardial stiffness as the cardiorespiratory fitness declined (Figure 2C). Conclusions Our data indicate that myocardial stiffness in cancer survivors is significantly increased. We hypothesize that this increased stiffness may at least in part explain the significantly reduced cardiorespiratory fitness in this population. Echocardiographic shear wave elastography appears as a promising non-invasive tool for early detection of diastolic dysfunction in follow-up of childhood cancer survivors.Figure 1.Figure 2.

Surface Wave Imaging Method Based on Spatial Autocorrelation Principle for Mining Microseismic Monitoring

Surface Wave Imaging Method Based on Spatial Autocorrelation Principle for Mining Microseismic Monitoring

A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding.

To enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts. A displacement-encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center-out golden-angle stack-of-stars sampling pattern was introduced for improved SNR and data incoherence. A combination of Hadamard encoding and interleaved multislab acquisition schemes was used to increase the acquisition efficiency of MRE data with multiple directions and phase offsets. A generalized parallel-imaging and compressed-sensing method was further applied to accelerate the acquisition process. The imaging results of the proposed sequence were compared with those from six gradient echo (GRE)/EPI/DENSE-based MRE sequences via phantom and brain acquisitions. The proposed sequence achieved a 6-fold acceleration compared with GRE MRE. With the application of a conventional parallel-imaging and compressed-sensing algorithm, the scanning speed was further accelerated by 8-fold, matching the speed of EPI-based MRE. Phantom tests revealed small variances in stiffness measurements across the seven sequences (< 9.23%). The proposed sequence exhibited a higher contrast-to-noise ratio (1.38) than the two EPI-based sequences (0.61/0.76) and similar to GRE-based sequences (1.34/1.22/1.58). Brain imaging validated the effectiveness of the proposed sequence in accurate stiffness estimation and distortion artifact avoidance. A rapid DENSE-based MRE sequence with interleaved multislab acquisition and Hadamard encoding was developed at a speed matching EPI-based sequences, without compromising SNR or introducing distortion artifacts.

Lamb Wave TDTE Super-Resolution Imaging Assisted by Deep Learning

Abstract Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy imaging. Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method (SCF-TFM), the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.

Novel Statistical Analysis Schemes for Frequency-Modulated Thermal Wave Imaging for Inspection of Ship Hull Materials

Novel Statistical Analysis Schemes for Frequency-Modulated Thermal Wave Imaging for Inspection of Ship Hull Materials

Beamforming-integrated neural networks for ultrasound imaging

Sparse matrix beamforming (SMB) is a computationally efficient reformulation of delay-and-sum (DAS) beamforming as a single sparse matrix multiplication. This reformulation can potentially dovetail with machine learning platforms like TensorFlow and PyTorch that already support sparse matrix operations. In this work, using SMB principles, we present the development of beamforming-integrated neural networks (BINNs) that can rationally infer ultrasound images directly from pre-beamforming channel-domain radiofrequency (RF) datasets. To demonstrate feasibility, a toy BINN was first designed with two 2D-convolution layers that were respectively placed both before and after an SMB layer. This toy BINN correctly updated kernel weights in all convolution layers, demonstrating efficiency in both training (PyTorch – 133 ms, TensorFlow – 22 ms) and inference (PyTorch – 4 ms, TensorFlow – 5 ms). As an application demonstration, another BINN with two RF-domain convolution layers, an SMB layer, and three image-domain convolution layers was designed to infer high-quality B-mode images in vivo from single-shot plane-wave channel RF data. When trained using 31-angle compounded plane wave images (3000 frames from 22 human volunteers), this BINN showed mean-square logarithmic error improvements of 21.3 % and 431 % in the inferred B-mode image quality respectively comparing to an image-to-image convolutional neural network (CNN) and an RF-to-image CNN with the same number of layers and learnable parameters (3,777). Overall, by including an SMB layer to adopt prior knowledge of DAS beamforming, BINN shows potential as a new type of informed machine learning framework for ultrasound imaging.

Pulse-Echo Ultrasound for Quantitative Measurements of Two Uncorrelated Elastic Parameters

ObjectiveThis paper describes the relationship between elastic tissue properties and strain and presents an initial investigation of pulse-echo ultrasound to measure two uncorrelated elastic parameters in tissue-mimicking phantoms. The two elastic parameters are the shear modulus, related to deformation of shape, and what we in the paper define as the nonlinear compressibility, related to deformation of volume. MethodsWe prepared tissue-mimicking phantoms containing lesions of variable shear modulus and variable nonlinear compressibility. An in-house framework for shear wave imaging was developed using ultrasound radiation force at 4.5 MHz to induce shear waves and plane wave imaging with pulses in a frequency band centered around 12.5 MHz to track the shear waves. For measurements of nonlinear compressibility, co-propagating dual-frequency pulse complexes at 0.7 MHz and 14 MHz were applied. Algorithms were implemented on a Verasonics Vantage ultrasound scanner and a custom-made multi-frequency ultrasound transducer was used. Mechanical indentation measurements were performed to validate ultrasound measurements of the shear modulus. For the nonlinear compressibility, ultrasound measurements were compared to results derived from the literature. ResultsWe found good agreement in elasticity results from ultrasound measurements and mechanical indentation as well as when comparing with results derived from the literature. ConclusionResults of the current investigation were promising. We plan patient studies involving thyroid lesions and liver steatosis to explore whether measurements of elastic parameters related both to shape deformation and volume deformation are useful in clinical practice.

Spectrum-Domain Plane Wave Imaging: A Novel Approach to Studying Multilayered Medium

Spectrum-Domain Plane Wave Imaging: A Novel Approach to Studying Multilayered Medium

Broadband nonlinear delay and sum (NL-DAS) for baseline-free damage imaging using a sparse sensor network

The Delay and Sum (DAS) method is a well-known damage imaging technique in sparse array guided wave imaging. However, the DAS method requires prior knowledge on the stable baseline state, which makes it ineffective under different operational or environmental conditions, e.g. temperature and moisture, and altered experimental settings, e.g. degraded bonding quality of sensors.This paper proposes a Nonlinear Delay and Sum (NL-DAS) method which does not require prior knowledge about the baseline state, but instead exploits the presence of nonlinear wave/defect interactions. Relatively high-power broadband sweep sine signals are supplied to a sparse sensor array to activate a multitude of nonlinear wave/defect interactions. Application of time–frequency filtering to the response signals allows the isolation of damage-induced broadband nonlinear responses. The isolated broadband nonlinear response signals are subsequently decomposed into a series of narrowband tone burst responses from which a set of damage maps are constructed. To improve the quality of the constructed damage maps, an automated framework is proposed to obtain the frequency-dependent directional group velocities without requiring prior knowledge on material properties. Finally, the resulting set of damage maps is fused into a single broadband NL-DAS damage map.The proposed broadband NL-DAS approach is demonstrated on a simulation dataset, generated with 3D Finite Element method, which is representative for a cross-ply carbon fiber reinforced polymer (CFRP) containing a delamination defect. The damage imaging performance is studied for different test conditions in terms of (i) signal-to-noise ratio, (ii) number of sensors and (iii) excitation bandwidth. Experimental validation is illustrated on a CFRP plate containing a barely visible impact damage, and on a CFRP A320 component with a disbond at one of the stiffeners.

A multi-scale deep residual network-based guided wave imaging evaluation method for fatigue crack quantification

Abstract As a promising structural health monitoring (SHM) technology, guided wave (GW) imaging is gaining increasing attention for crack monitoring of aircraft structures. However, actual fatigue crack propagation is a complex dynamically evolving process affected by various variabilities. It is still challenging to accurately track and quantify the dynamic fatigue crack propagation with GW imaging methods. Therefore, in order to achieve more accurate fatigue crack quantification, this paper proposes a multi-scale deep residual network-based GW imaging evaluation method. A convolutional neural network (CNN) is utilized to evaluate the entire pixel distribution of GW imaging maps to fuse damage-related information from multiple GW monitoring paths. By designing multi-scale convolutional kernels and deep residual learning, a robust quantitative image feature extraction is ensured with the dynamic evolution process of fatigue crack growth and the performance degradation is avoided as the CNN goes deeper, thereby improving the quantification accuracy. The method is validated on a fatigue test of landing gear beams, which are important load-carrying aircraft structural components. The results demonstrate that the proposed method can extract multi-scale crack length-related features and accurately track fatigue crack propagations. For batch specimens, the maximum quantification error is reduced from the original 6.1mm to 1.6mm, marking a significant improvement.

Detection of natural pulse waves (PWs) in 3D using high frame rate imaging for anisotropy characterization

IntroductionNumerous studies have shown that natural mechanical waves have the potential to assess the elastic properties of the myocardium. When the Aortic and Mitral valves close, a shear wave is produced, which provides insights into tissue stiffness. In addition, the Atrial Kick (AK) generates a wave similar to Pulse Waves (PWs) in arteries, providing another way to assess tissue stiffness. However, tissue anisotropy can also impact PW propagation, which is currently underexplored. This study aims to address this gap by investigating the impact of anisotropy on PW propagation in a phantom.MethodsTube phantoms were created using Polyvinyl Alcohol (PVA). Anisotropy was induced between two sets of two freeze-thaw cycles by stretching and twisting the material. The study first tests and validates the procedure of making helical anisotropic vessel phantoms using the shear wave imaging technique (by estimating the shear wave speed at different probe angles). Using plane wave ultrasound tomography synchronized with a peristaltic pump, 3D high frame rate imaging is performed and used to detect the 3D propagation pattern of PW for each manufactured vessel phantom. Finally, the study attempts to extract the anisotropic coefficient of the vessel using pulse wave propagation angle.ResultsThe Shear wave imaging results obtained for the isotropic vessel show very similar values for each probe angle. On the contrary, the results obtained for the axial anisotropy vessel show a region with a higher shear wave speed at about 0°, corresponding to the long axis of the vessel. Finally, the results obtained for the helical anisotropy depicted increasing shear wave velocity value from −20° to 20°. For the axial phantom, the wavefront of the pulse wave is perpendicular to the long axis of the vessel, while oriented for the helical anisotropic vessels phantom. The pulse wave propagation angle increased with the number of twists made during the vessel manufacturing.DiscussionThe results show that anisotropy can be induced in PVA vessel phantoms by stretching and twisting the material in freeze-thaw cycles. The findings also suggest that vessel anisotropy affects pulse wave propagation angles. Estimating the pulse wave propagation angle may be interesting in characterizing tissue anisotropy in organs where such waves are naturally present.

Fourier-based beamforming for 3D plane wave imaging and application in vector flow imaging using selective compounding

Objective. Ultrafast ultrasound imaging using planar or diverging waves for transmission is a promising approach for efficient 3D imaging with matrix arrays. This technique has advantages for B-mode imaging and advanced techniques, such as 3D vector flow imaging (VFI). The computation load of the cross-beam technique is associated with the number of transmit anglesmand receive anglesn. The full velocity vector is obtained using the least square fashion. However, the beamforming is repeatedm × ntimes using a conventional time-domain delay-and-sum (DAS) beamformer. In the 3D case, the collection and processing of data from different beams increase the amount of data that must be processed, requiring more storage capacity and processing power. Furthermore, the large computation complexity of DAS is another major concern. These challenges translate into longer computational times, increased complexity in data processing, and difficulty in real-time applications.Approach. In response to this issue, this study proposes a novel Fourier domain beamformer for 3D plane wave imaging, which significantly increases the computational speed. Additionally, a selective compounding strategy is proposed for VFI, which reduces the beamforming process fromm × ntom(wheremandnrepresent the number of transmission and reception, respectively), effectively shortening the processing time. The underlying principle is to decompose the receive wavefront into a series of plane waves with different slant angles. Each slant angle can produce a sub-volume for coherent or selective compounding. This method does not rely on the assumption that the plane wave is perfect and the results show that our proposed beamformer is better than DAS in terms of resolution and image contrast. In the case of velocity estimation, for the Fourier-based method, only Tx angles are assigned in the beamformer and the selective compounding method produces the final image with a specialized Rx angle.Main results. Simulation studies andin vitroexperiments confirm the efficacy of this new method. The proposed beamformer shows improved resolution and contrast performance compared to the DAS beamformer for B-mode imaging, with a suppressed sidelobe level. Furthermore, the proposed technique outperforms the conventional DAS method, as evidenced by lower mean bias and standard deviation in velocity estimation for VFI. Notably, the computation time has been shortened by 40 times, thus promoting the real-time application of this technique. The efficacy of this new method is verified through simulation studies andin vitroexperiments and evaluated by mean bias and standard deviation. Thein vitroresults reveal a better velocity estimation: the mean bias is 2.3%, 3.4%, and 5.0% forvx,vy, andvz, respectively. The mean standard deviation is 1.8%, 1.7%, and 3.4%. With DAS, the evaluated mean bias is 9.8%, 4.6%, and 6.7% and the measured mean standard deviation is 7.5%, 2.5%, and 3.9%.Significance. In this work, we propose a novel Fourier-based method for both B-mode imaging and functional VFI. The new beamformer is shown to produce better image quality and improved velocity estimation. Moreover, the new VFI computation time is reduced by 40 times compared to conventional methods. This new method may pave a new way for real-time 3D VFI applications.

Complex Pseudo 3D Auto-Correlation Network for High-Quality Single-Angle Plane Wave Imaging

Abstract Deep learning has shown potential as an effective beamformer for improving the image quality of plane wave imaging (PWI). But most existing deep learning methods cannot directly handle the complex in-phase and quadrature (IQ) data. And noise in ultrasound signals would significantly damage the performance of regular convolution networks. To address these challenges, we proposed the Complex Pseudo 3D Auto-Correlation Network (CP3AN) which combined complex convolution and pseudo 3D auto-correlation blocks (P3AB) to directly map delayed IQ data from 0° plane wave into PWI pixels. The complex convolution could fully utilize the envelope and phase information of IQ data, while the P3AB used 1D convolution to extract noise in channel and space dimensions, allowing the network to prioritize valid signals with extremely low computational cost. We evaluated the performance of CP3AN through numerical simulations, phantom experiments, and in-vivo experiments, which showed comparable metrics with minimum variance (MV), including contrast (CR), contrast-to-noise ratio (CNR), generalized contrast-to-noise ratio (GCNR), lateral, and axial full-width half maximum (FWHM) at -9.60 dB, 1.12, 0.65, 319 um, and 344 um, respectively. The CP3AN achieved a low computational cost of 0.11 M floating-point operations (FLOPs), significantly lower than the MV or other compared deep learning-based methods. Our proposed method provided a promising solution for improving single-angle PWI imaging, particularly in situations where high frame rates are necessary.

Study on ultrasonic lamb wave testing technology of honeycomb sandwich structures

Abstract Honeycomb sandwich composite panels have complex structures, large sizes, many types of defects, and significant sound attenuation, which lead to low efficiency and poor sensitivity of ultrasonic non-destructive testing. In the study, a non-linear ultrasonic lamb wave testing technique for honeycomb sandwich composite panels is proposed. Firstly, the anisotropic propagation characteristics and modal structure of lamb waves in honeycomb sandwich composite panels are analyzed, and the lamb wave modes with non-linear ultrasonic accumulation are excited. Secondly, the data extraction method and imaging algorithm of non-linear ultrasonic lamb wave imaging testing are proposed. Finally, an enhanced imaging algorithm is proposed to improve image quality based on the sliding window algorithm. Based on the comparative analysis of air-coupled ultrasonic testing images and metallographic tests, it can be seen that non-linear ultrasonic lamb wave testing signals can be used to characterize the damage distribution characteristics of honeycomb sandwich composite panels, display debonding and weak debonding defects, and have the advantages of being able to perform on the same side testing and high efficiency, which can be used for non-destructive testing of honeycomb sandwich composite panels.

Stand-off concealed firearm detection using motion tracking and convolutional neural networks

&lt;p&gt;The standoff detection of concealed firearms is crutial in managing public security in public spaces. Currently employed standoff concealed weapon detection techniques employ electromagnetic wave imaging which has been found to be extremely slow and may require expensive hardware and may not be applicable in public open spaces. Inorder to maintain safety in open spaces, artificial intelligence enabled video surveillance systems have been widely adopted. This poses an opportunity to explore video surveillance cameras as concealed weapon detectors. A review of existing video surveillance based automated weapon detection approaches discovered that the focus was on the detection of unconcealed firearms leaving a gap in the detection of concealed firearms. This study addresses the aforementioned gap by providing a standoff concealed firearm detection approach on video based on skeletal-based human motion tracking and convolutional neural networks. The motion of armed and unarmed persons was tracked using a depth camera and further classified using convolutional neural networks model. The developed model reported 100% accuracy, precision and recall scores. These results outperformed results obtained from traditional machine learning models therefore highlighting the superior capability of the proposed approach for concealed firearm detection on video to complement the efforts of human video surveillance operators.&lt;/p&gt;

A novel explicit optimized scheme for numerical simulation of elastic-wavefield separation

Abstract Numerical simulation of elastic wave equation helps us better understand the information of underground structures and elastic wave imaging has attracted the widespread attention of researchers. Using elastic wave imaging requires separating the compressional and shear wavefields. Therefore, we develop a novel explicit optimized scheme to simulate the separated elastic wavefield. We construct a kind of 1-norm objective function directly utilizing the dispersion error and employ the simulated annealing algorithm to acquire improved finite-difference operators, whose optimal coefficients can effectively suppress spatial numerical dispersion. Meanwhile, we introduce a rotated staggered-grid (RSG) approach to enhance computational stability. Then, our proposed scheme also called the optimized RSG approach is applied to the elastic wave equations and decoupled elastic wave equations to simulate the decoupled compressional and shear wavefield propagation. Numerical dispersion analysis is consistent with numerical results. The waveform comparison shows that the optimized RSG approach possesses higher accuracy, and several complex models are used to validate the applicability and effectiveness of the presented scheme.

Imaging the intramuscular pressure of living muscles with shear waves

Shear wave elastography (SWE) is an innovative method that allows for the nondestructive and quantitative characterization of muscular mechanical properties. This method finds extensive utility in fields such as sports medicine, sports rehabilitation, and the diagnosis of muscle-related ailments. Existing studies have demonstrated the promise of SWE in probing intramuscular pressure (IMP), a factor intimately tied to muscular physiological functions and the onset of certain diseases. Nonetheless, there remains a lack of a SWE method grounded in an appropriate biomechanical model capable of effectively imaging IMP in vivo. Addressing this issue, we propose a shear wave imaging method relaying on a porohyperelastic model encompassing well-defined parameters for both muscular active behavior and intramuscular pressure. Drawing upon wave motion analysis, we establish a correlation between shear wave velocities and IMP in analytical form. This theoretical solution on one hand help understand the interplay between IMP and muscle active stress and the impact of muscle contraction on IMP. On the other hand, it enables us to develop an elastography method to assess IMP in vivo. We conducted a series of experiments to underscore the applicability of our theory and elastography method. Ex vivo experiments were performed on porcine muscles, while in vivo tests were carried out on human skeletal muscles. The results from the ex vivo tests validate the efficacy of our method. Meanwhile, the in vivo outcomes suggest that our approach holds potential to assess the variation of IMP with muscle fatigue and injuries, inspect intramuscular injections, and diagnose acute and chronic compartment syndrome.

Simultaneous Optical Imaging of Gastric Slow Waves and Contractions in the In Vivo Porcine Stomach.

Gastric peristalsis is governed by electrical "slow waves" generally assumed to travel from proximal to distal stomach (antegrade propagation) in symmetric rings. While alternative slow wave patterns have been correlated with gastric disorders, their mechanisms and how they alter contractions remain understudied. Optical electromechanical mapping, a developing field in cardiac electrophysiology, images electrical and mechanical physiology simultaneously. Here, we translate this technology to the in-vivo porcine stomach. Stomachs were surgically exposed and a fluorescent dye (di-4-ANEQ(F)PTEA) that transduces the membrane potential (Vm) was injected through the right gastroepiploic artery. Fluorescence was excited by LEDs and imaged with one or two 256x256 pixel cameras. Motion artifact was corrected using a marker-based motion tracking method and excitation ratiometry, which cancels common-mode artifact. Tracking marker displacement also enabled gastric deformation to be measured. We validated detection of electrical activation and Vm morphology against alternative non-optical technologies. Non-antegrade slow waves and propagation direction differences between the anterior and posterior stomach were commonly present in our data. However, sham experiments suggest they were a feature of the animal preparation and not an artifact of optical mapping. In experiments to demonstrate the method's capabilities, we found that repolarization did not always follow at a fixed time behind activation "wavefronts," which could be a factor in dysrhythmia. Contraction strength and the latency between electrical activation and contraction differed between antegrade and non-antegrade propagation. In conclusion, optical electromechanical mapping, which simultaneously images electrical and mechanical activity, enables novel questions regarding normal and abnormal gastric physiology to be explored.