Wave Imaging Research Articles

RADD-CycleGAN: unsupervised reconstruction of high-quality ultrasound image based on CycleGAN with residual attention and dual-domain discrimination.

Plane wave imaging (PWI) is fast, but limited by poor imaging quality. Coherent plane wave compounding (CPWC) improves image quality but decrease frame rate. In this study, we propose a modified CycleGAN model that combines a residual attention module with a space-frequency dual-domain discriminator, termed RADD-CycleGAN, to rapidly reconstruct high-quality ultrasound images. To enhance the ability to reconstruct image details, we specially design a process of hybrid dynamic and static channel selection followed by the frequency domain discriminator. The low-quality images are generated by the 3-angle CPWC, while the high-quality images are generated as real images (ground truth) by the 75-angle CPWC. The training set includes unpaired images, whereas the images in the test set are paired to verify the validity and superiority of the proposed model. Finally, we respectively design ablation and comparison experiments to evaluate the model performance. Compared with the basic CycleGAN, our proposed method reaches a better performance, with a 7.8% increase in the peak signal-to-noise ratio (PSNR) and a 22.2% increase in the structural similarity index measure (SSIM). The experimental results show that our method achieves the best unsupervised reconstruction from low quality images in comparison with several state-of-the-art methods.

Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder

Thermal wave imaging uses the temporal temperature distribution over the object’s surface for subsurface analysis. However, the noise generated during experimentation corrupts this temporal history and hampers the detection of defect signatures. As denoising of the temporal thermal history enhances the defect detectability, this study offers a stacked denoising convolution autoencoder (SDCAE) in frequency-modulated thermal wave imaging with one-dimensional convolution layers to reduce noise in temporal thermal evolution and train high-level features resulting in improved defect signs. Experimental results on mild steel and carbon fiber reinforced polymer specimens with different sizes of defects at various depths demonstrate that integrating temporal denoising and deep feature learning techniques into a single novel framework significantly improved defect detectability. In addition, defect signal-to-noise ratios of the denoised thermal data and latent space of the proposed model compared to conventional autoencoder and dimensionality reduction techniques recommend the superiority of the proposed method.

Rail bottom rust localisation imaging monitoring using ultrasonic-guided wave combining with variational modal decomposition and principal component analysis

ABSTRACT This paper presents a high-precision ultrasonic guided wave imaging monitoring technique for rust monitoring at the bottom of rails, which mainly utilizes piezoelectric sensing arrays combined with an ellipsoidal probability-weighted imaging method. A combination of Variational Mode Decomposition (VMD) and Principal Component Analysis (PCA) was utilized to enhance imaging monitoring accuracy. The feasibility and effectiveness of the method were validated through the establishment of simulation models with varying rust depths and the construction of experimental systems. The region with the highest probability value in the monitoring image is extracted, and the relative distance between the predicted and actual rust locations（RD_pa） is calculated to assess the accuracy of damage localization. The results of the simulation and experimental studies show that the RD_pa error values after VMD-PCA treatment are 18mm and 20mm, respectively, and these values exhibit significantly smaller numerical errors compared to those of the untreated, VMD-only, and PCA-only treatments. The probability value extracted from the predicted position image, processed using the VMD-PCA feature enhancement algorithm, increases concurrently with the depth of the rust pits. This increase reflects the severity of rust damage to the rail bottom, facilitating a quantitative assessment of the rust extent on the rail.

Applicable Near-Field Millimeter Wave Imaging Technology for Human Body Security Based on Scanning 1D Non-uniform Sparse Arrays

Applicable Near-Field Millimeter Wave Imaging Technology for Human Body Security Based on Scanning 1D Non-uniform Sparse Arrays

Electroacupuncture with extensor exercise improves the contraction elastic density of quadriceps in short and long term for knee osteoarthritis.

Extensor training improves the quadriceps contraction intensity ofknee osteoarthritis. But the duration of effects is limited. This study aimed to assess whether electroacupuncture (EA) with extensor training (EA + E) has better short-and long-term effects than simple extensor training (E). Fifty-four patients were categorized into EA + E and E groups. Elastic shear wave imaging was employed to ascertain the quadriceps elastic modulus values (EMV) at different time points and extension angles. WOMAC scale was used to measure knee function. Compared to before EA, the vastus lateralis (VL) EMV with 180° knee extension immediately after EA increased significantly. After 1month, the vastus medialis (VM) EMV with 180° extension increased and WOMAC scores decreased significantly than immediately after EA. The EMV of the VM and VL of group EA + E increased, and WOMAC scores decreased significantly compared to group E after 1month. After 6months, EMV of the VM and VL in EA + E group significantly increased, and WOMAC scores significantly decreased compared to E group. In short- and long-term, EA with extensor training enhances the contraction strength of the quadriceps and knee function for OA. This prospective single-center randomized controlled trial was approved by the Review Committee of the First Affiliated Hospital of Sun Yat-sen University (Registration date: 28/02/2023, Ethical Number: [2023] 005; The Clinical trial registration number: ChiCTR2300076651. • The contraction strength of the quadriceps femoris muscle in OA was detected with shear wave elastic ultrasound, and electroacupuncture immediately enhanced the contraction strength. • After the completion of electroacupuncture treatment, the quadriceps strength and the function of knee joint in OA can be continuously improved, which will last for 6months. • In short and long term, electroacupuncture with extensor training has better enhancement of quadriceps contraction strength of and knee function for OA than extention only.

Seasonal variation and fundamental characteristics of baroclinic tides in the Arabian Gulf

In the Arabian Gulf, baroclinic tides significantly impact physical and biological phenomena, shaping the region’s marine ecosystem. This study provides the first high-resolution investigation of the baroclinic tides using the three-dimensional, nonhydrostatic MIT general circulation model (MITgcm). The simulation outputs were validated against data from Oregon State University’s Tidal Inversion Software (OTIS), tidal stations, and mooring observations, showing high agreement and confirming the model’s accuracy. We further examined the characteristics of baroclinic and barotropic tides, identifying four key regions with high-intensity baroclinic tides based on energy budget analysis: 1) the narrow Strait of Hormuz; 2) the strait’s entrance along the trough, including the islands Farur, Siri, Abumusa, and Greater Tunb; 3) the central axial trough area with sea-bottom ridges; and 4) the northern area following a secondary ridge. Most baroclinic tides dissipate quickly due to interactions with complex bottom topography and do not propagate far from their sources. Significant seasonal variation shows that in winter, only the Strait of Hormuz and the area around the four islands showed significant baroclinic tidal energy, likely due to well-mixed upper layer seawater that hinders thermocline disturbances. The strong correlation between the simulation results and satellite images of internal waves suggests that intense baroclinic tides generate internal solitary waves in the Gulf.

Lamb wave imaging via dual-frequency fusion for grating lobe effect compensation

In Lamb wave imaging based on a phased array, higher frequencies narrowband excitation pulses enable more precise damage detection and localization. However, due to the size constraints of individual transducer elements, the spacing between array elements may exceed half the wavelength of the excitation signal. This can lead to a grating lobe effect. To overcome this limitation, a Lamb wave imaging method via dual-frequency fusion for grating lobe effect compensation is proposed in this study. Analyses indicate that the grating lobe effect may introduce artifacts or distortions in the imaging results. This method utilizes two frequencies of narrowband excitation pulses for imaging and subsequently fuses the results. By doing so, the imaging artifacts caused by the grating lobes produced by high-frequency narrowband excitation pulses are effectively compensated. The proposed method is validated through simulations and experiments on an aluminum plate, showing superior accuracy, contrast, and imaging quality.

Abstract 4113716: Natural shear wave imaging as a predictor for left ventricular pressures: Can they reflect raised filling pressures?

Background: Current conventional echocardiographic assessment of diastolic function relies on predicting left ventricular (LV) filling pressure as a surrogate of diastolic function. High frame rate (HFR) Shear wave (SW) imaging is emerging as an innovative parameter for assessing myocardial stiffness, a key determinant of diastolic function. Natural SW are induced by valve closure (i.e. mitral valve closure (MVC)). Little is known so far, to which extent SWs could reflect changes in diastolic LV filling pressures. Purpose: We aimed at investigating the relationship between natural SW after MVC and LV diastolic pressures in an attempt to set up a cut-off value beyond which, elevated LV pressure can be suspected indicating diastolic dysfunction. Methods: Thirty patients (mean age 68.4±9.9 years) scheduled for clinically indicated left heart catheterization were prospectively recruited. Left ventricular end-diastolic pressure (LVEDP) was measured during the catheterization. Immediately afterwards, conventional as well as HFR (1167 ±86 Hz) echocardiography was performed. We drew an anatomical M-mode along the anteroseptal wall in the parasternal long axis view from base to apex and the display was colour coded for tissue acceleration. SWs appeared immediately after MVC as tilted green bands and their propagation velocity was measured semi-automatically (Figure 1) . Results: LVEDP in our cohort ranged from 7.5 to 29.5 mmHg. From conventional echocardiographic parameters, only mitral inflow to mitral relaxation velocity ratio (E/e’) correlated moderately with LVEDP (r=0.51, p=0.006). SW velocities after MVC showed strong positive correlation with LVEDP (r=0.78, p<0.001) (Figure 2) . A cut-off value for SW velocity of 5.8 m/s showed an excellent ability to discriminate normal from elevated LVEDP (AUC: 0.98; Sensitivity: 90%, specificity: 100%, accuracy: 97%) (Figure 3) . Conclusions: Our data showed that SW velocities at end-diastole after MVC could predict elevated LV filling pressures accurately. Natural shear wave imaging appears as a promising innovative tool for assessment of diastolic function.

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.

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

Abstract As a promising structural health monitoring 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 6.1 mm to 1.6 mm, marking a significant improvement.

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.

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.

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.