Wave Velocity Estimation Research Articles

Interpolation time-optimized aortic pulse wave velocity estimation by 4D flow MRI

Four-dimensional flow magnetic resonance imaging-based pulse wave velocity (4D flow PWV) estimation is a promising tool for measuring regional aortic stiffness for non-invasive cardiovascular disease screening. However, the effect of variations in the shape of flow waveforms on 4D flow PWV measurements remains unclear. In this study, 4D flow PWV values were compared using cross-correlation algorithm with different interpolation times (iTs) based on flow rate and beat frequency. A critical iT (iTCrit) was proposed from in vitro study using flexible and stiff phantom models to simultaneously achieve a low difference and a low computation time. In vivo 4D flow PWV values from six healthy volunteers were also compared between iTCrit and the conventionally used interpolation time of 1 ms (iT1 ms). The results indicated that iTCrit reduced the mean difference of in vitro 4D flow PWV values by 19%, compared to iT1 ms. In addition, iTCrit measured in vivo 4D flow PWV, showing differences similar to those obtained with iT1 ms. A difference estimation model was proposed to retrospectively estimate potential differences of 4D flow PWV using known values of PWV and the used iT. This study would be helpful for understanding the differences of PWV generated by physiological changes and time step of obtained flow waveforms.

Rational function neural networks for learning rock physics models from field data

Abstract Seismic wave velocity estimation is critical for understanding Earth's internal structure. Traditional rock physics models require careful physical assumptions and mathematical derivations, often facing challenges when applied to complex field data. Empirical formulas, while simple, lack a solid physical foundation. To address these limitations, we propose a data-driven approach using rational function neural networks (RafNN) for rock physics modelling. By analysing logging data, RafNN establishes a rational equation capturing the interdependencies among rock modulus, matrix stiffness, porosity, and fluid. The results show that RafNN accurately extracts the Gassmann's equation when the training data adheres to its constraints. Moreover, RafNN can derive general models from logging data that deviate from the Gassmann's equation. These data-driven models exhibit lower prediction errors while maintaining consistency with Gassmann's model. RafNN's adaptability to field data variability is a key advantage, facilitating better comprehension of the underlying mathematical and physical principles. Additionally, we explore the relationship between modulus, porosity, and compressibility, shedding light on the physical interpretation of RafNN models. Notably, RafNN derives analytical models directly from field data, reducing reliance on mathematical derivations and physical assumptions. Although further research is needed to understand the convergence theory of RafNN, this study presents a promising approach for data-driven rock physics modelling. It contributes to the exploration of Earth's heterogeneous structure and advances the field of seismic wave velocity estimation.

Variability in parameter estimation for a tall steel-frame building

The between-event variability in identified shear wave velocities of fitted four-layer beam models in the response of a 54-story steel-frame building during nine earthquakes over a period of 27 years (1992–2019) is investigated. Recorded response and simulated response by its digital twin (linear finite element model with fixed base) were used as input, that is, the real structure and its digital twin were identified. Two surrogate models were fitted, a Timoshenko beam and a shear beam. The waveform inversion of impulse response functions method, developed earlier by the authors, was used to fit the beam models. The coefficients of variation (C.V.) of the between-events variability in the wave velocity estimates of the four layers were analyzed and compared for the different cases of data (real structure versus digital twin), different surrogate models, and different locations on the floor where the motions were observed. The results showed that the variability of the digital twin was affected by the contamination of the response by torsion, which is not accounted for in the beam models, by the degree of geometric regularity of the structure and by how closely the beam model represented the nature of the building deformation (balance of shear and bending deformation). These effects were minor in comparison to the nonlinear effects (recoverable nonlinearity and permanent stiffness degradation), which were not present in the digital twin response. The C.V. for the real structure (about 3%–5%) was comparable for both beam models fitted.

Value of estimating pulse wave velocity compared to SCORE in cardiovascular risk stratification in community pharmacies

ObjectivesArterial stiffness is considered to be an intermediate marker with independent prognostic value. The objective of this study is to assess whether the estimation of arterial stiffness can improve CV risk stratification compared to SCORE in patients at community pharmacies. MethodsObservational prospective epidemiological study in which consecutive individuals entering a participating Community Pharmacy are offered a voluntary measurement of blood pressure and estimation of pulse wave velocity by oscillometry (AGEDIO, IEM®) to stratify their CV risk according to SCORE compared to the use of arterial stiffness. ResultsAfter nine months of recruitment, data from 923 patients (570 women, 353 men) were collected. 16/122 (13.1%) patients under 40 years and 72/364 (19.8%) over 65 years of age presented pathological stiffness and could be classified as high-risk, even though being out of the age-range of SCORE. Of the 437 (47.3%) patients who were susceptible to calculating SCORE, 42/437 patients (9.6%) presented pathological arterial stiffness. Cholesterol values were available in 281 patients (64.3%). Among them, according to SCORE, only 6 (2.1%) fell into the high-risk category. ConclusionsMore than half of the subjects who randomly enter a community pharmacy had ages that make it impossible to calculate the CV risk by SCORE. Among them, arterial damage was detected in 18.1%. Of the other half, 9.6% presented arterial damage and, therefore, high CV risk, when SCORE only detected it in 2.1%. Therefore, estimating arterial stiffness in community pharmacies markedly improves detection of high CV risk compared to SCORE.

Correction of evanescent wave influence on the flexural wave velocity and wavelength estimation based on a mode shape function

Correction of evanescent wave influence on the flexural wave velocity and wavelength estimation based on a mode shape function

Shallow S-wave Velocity Profile Estimation using Surface Velocity and Microtremor HVSR with a Linear Velocity Increase Approach

We propose a simple method for 1D S-wave velocity (Vs) profile estimation using a measured surface S-wave velocity (V1) and peak frequency of the observed microtremor horizontal-to-vertical spectral ratio (HVSR). In this method, the S-wave velocity profile is presented as linear velocity increase with depth in sediments over a bedrock layer that has a given constant S-wave velocity. Thus, the profile can be parameterized with the measured surface S-wave velocity and the velocity gradient. The gradient can be estimated based on the agreement of the peak frequencies of the observed microtremor HVSR and the theoretical ellipticity of the fundamental mode of the Rayleigh wave. We examined the applicability of the proposed method using numerical tests as well as application to actual data at five sites in the Bandung Basin, Indonesia, where observed Rayleigh wave phase velocities from microtremor array surveys were available. The applicability was confirmed in numerical tests using sample models of soil profiles in the basin. Actual application indicated the appropriateness of the estimated S-wave velocity profiles due to the similarity of their theoretical Rayleigh wave phase velocities with the observed Rayleigh wave phase velocities. Since the proposed method needs prior confirmation of the linear increase of the S-wave velocity, it is suitable for use in spatial interpolation of shallow S-wave velocity profiles with simple data acquisition.

Simultaneous nondestructive estimation of thickness and longitudinal wave velocity of adhesive layers in adhesive joints through air-coupled ultrasonic testing

This paper proposes a nondestructive method for the simultaneous estimation of the thickness and longitudinal wave velocity of an adhesive layer using a transmission coefficient spectrum, including amplitude and phase spectra measured via air-coupled ultrasonic testing to confirm adhesive joint reliability. The method correctly estimated the values of the adhesive layer from the transmission coefficient spectrum experimentally, unlike the conventional estimation using only the amplitude spectra. The absolute values of the estimation errors of thickness and longitudinal wave velocity obtained using this method were 1.3–12.1% and 0.4–6.1%, respectively. The proposed method can be useful for the quality assurance of adhesive joints.

Quasi-site-specific prediction of shear wave velocity from CPTu

A rapid and reasonable estimation of the local site shear wave velocity (Vs) profile is indispensable for site assessment. For soil deposits, researchers have developed numerous empirical correlations between Vs and piezocone penetration test (CPTu) based on a global/regional (generic) database. However, these transformations are usually biased for local sites because the generic database contains all available data without regard to site-specific differences, and the transformation uncertainty is usually non-negligible due to the complicated geology factors and measurement errors. Moreover, it is challenging to construct a site-specific correlation due to the sparse in-situ measurements. To this end, this study develops a quasi-site-specific CPTu-Vs transformation model that algorithmically combines both the generic database and site-specific measurements to provide reliable Vs prediction with uncertainty description. A generic database containing 3116 cases of 6 parameters is collected from different countries, including corrected cone tip resistance, sleeve frictional resistance, depth, soil behavior index, pore pressure ratio, and Vs. As the significant impact of soil type on CPTu-Vs correlation, independent Johnson transformation (JS) for different soil types is first adopted to normalize the generic database as a whole, ensuring the constructed model is applicable to any soil type site. Three state-of-art quasi-site-specific models are investigated for CPTu-Vs transformation: probabilistic multiple regression (PMR), the hybridization method (HYD), and the hierarchical Bayesian model (HBM). Empirical correlations and the recently-developed data-driven model (XGBoost) are also considered to illustrate the novelty of the quasi-site-specific model. The applications on three sites with different soil types show that considering soil type in JS improves model accuracy and reduces the transformation uncertainty. The HBM with multiple basic CPTu parameters performs best regarding accuracy, uncertainty, and robustness.

Long-term ambient seismic interferometry for constraining seasonal subsurface velocity variations in urban settings: a distributed acoustic sensing (DAS) case study

SUMMARY Ambient seismic interferometry of distributed acoustic sensing (DAS) data acquired on optical fibre arrays is an increasingly common approach for subsurface investigation. The fixed infrastructure and low maintenance costs of commodity telecommunications fibre also supports cost-effective DAS-based seismic monitoring solutions over extended periods of time—especially when using repurposed telecommunication fibre infrastructure in urban settings. To investigate whether ambient waveform data acquired on such an urban DAS array are sensitive to seasonal subsurface variations, we present a case study using ‘semi-continuous’ DAS time-series data with hourly 150 s sampling windows that were acquired over a 10-month interval in the central business district of Perth, Australia. We apply a cross-coherence analysis to transform pre-processed ambient waveform data into sliding-window weekly interferometric virtual shot gathers (VSGs). We then use these data volumes to compute time-lapse velocity–dispersion panels, which we input to a multichannel analysis of surface waves (MASWs) to generate depth-averaged S-wave velocity estimates of the top 30 m ($V_{S_{30}}$ ). Our time-lapse analyses show that weekly stacked interferometric VSGs exhibit up to 5.8 per cent variations in observed surface wave traveltimes whereas the MASW inversion results capture up to 9.4 per cent variations in $V_{S_{30}}$ estimates between the winter and spring months. We note that these observations are inversely correlated with time-averaged rainfall patterns in the Perth Metro region and are likely attributable to the associated seasonal variations in near-surface groundwater content. Overall, our analysis suggests that semi-continuous ambient seismic monitoring on urban DAS fibre arrays is a computational tractable acquisition strategy that records data volumes useful for monitoring the seasonal variability of groundwater resources beneath urban centres as well as potentially other time-lapse subsurface behaviour occurring over calendar time.

Shear-wave velocity estimation based on rock physics modelling of a limestone gas reservoir in the Pannonian Basin

In the Pannonian Basin, especially in Croatia, there are a small number of wells for which acquired shear-wave (S-wave) velocities have been obtained. Often, quantitative interpretation relies only on compressional-wave (P-wave) velocity data, and S-wave velocity must be modelled. S-wave velocity estimation in combination with other petrophysical data is essential for detailed reservoir characterization. P- and S-wave velocities enable seismic modelling of different saturation states in a reservoir. This paper demonstrates a workflow for S-wave velocity estimation where S-wave velocity data are absent in the gas field and neighbouring fields with the same lithology, based on the Kuster–Toksöz and Xu–Payne models applied to the Pannonian Basin limestone reservoir. The results are calibrated with the P-wave velocity obtained from borehole data and the vertical seismic profile (VSP) S-wave interval velocity. Although rock physics models are idealized analogues of real rocks, a very good correlation was obtained between the modelled and measured P-wave velocity, as well as between the modelled S-wave velocity and the VSP interval velocity. The study also illustrates the problem of defining the pore aspect ratio in zones of increasing shale content. Due to the limited research on the limestones of the Pannonian Basin, these results enable a better understanding of the seismic parameters of the Pannonian Basin limestones. The results indicate that the proposed workflow gives an adequate estimation of S-wave velocities.

Examination of one-dimensional S-wave velocity structure using strong-motion data for high-seismic-intensity area during the 2018 Hokkaido Eastern Iburi earthquake

The Yufutsu Plain, a sedimentary basin surrounded by mountains, is located in the southern part of the Ishikari–Yufutsu Lowlands, Hokkaido, Japan. The Hidaka arc–arc collision zone, located in the eastern part of the Yufutsu Plain, forms the Hidaka Mountain range in central Hokkaido, with the Ishikari–Teichi–Toen Fault Zone of the Ishikari–Yufutsu Lowlands on the west side, which forms part of a major geological boundary that extends in the north–south direction. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) occurred at 03:08 JST on September 6, 2018, in this arc–arc collision zone. The K-NET HKD126 station in Mukawa Town, which is close to the severely damaged basin margin area, recorded strong-motion data with strong power for a predominant frequency of 0.5–1.0 Hz during the main shock. The peak ground acceleration was 661 cm/s2 in the east–west direction. The site amplification characteristics of the shallow S-wave velocity structure, which was estimated from microtremor array observations and surface wave explorations, were one of the causes of this strong ground motion. It is essential to accurately estimate the depth of the seismic bedrock and basin margin to evaluate the long period of large earthquakes. In this study, we used strong-motion data recorded at HKD126 and three temporary strong-motion stations near the basin margin area to tune the deep S-wave velocity structure. First, we performed microtremor array observations and surface wave explorations to estimate the S-wave velocity structure to a depth of 1 km beneath the station at the hill site because a detailed shallow structure is not available for this site. Then, with a combination of the estimated S-wave velocity structure and the existing structure, we tuned the deep structures based on an autocorrelation function analysis using strong-motion data. The validity of the estimated structures from the shallowest depth to the seismic bedrock was verified based on the differences between the observed arrival time difference and theoretical travel time difference for the S-wave initial motion. We estimated the seismic bedrock of the four stations to be at a depth of 7–10 km. In addition, an autocorrelation function analysis suggests topological bedrock undulations.Graphical

Trans-dimensional inversion of multimode seismic surface wave data from a trenched distributed acoustic sensing survey

SUMMARYSeismic data acquired from surface-deployed distributed acoustic sensing (DAS) fibre are broad band and typically dense spatially sampled. Corresponding to these features, compared with geophone data, the low-frequency components in DAS data show higher signal-to-noise ratio and multimode dispersion curves by broad-band DAS data exhibit a higher resolution, which increases the investigation depth of near-surface structures and enhances identification and picking of dispersion curves, respectively. Therefore, DAS data are ideal for the estimates of reliable and highly resolved near-surface velocity profiles. As surface-wave dispersion inversion (SWD) is a natural scheme for near-surface investigation, in this study we have formulated a DAS-SWD inversion in which multiple SWD modes are extracted from the DAS data, and are used as input to a trans-dimensional (TD) inversion procedure, in which the number of subsurface layers is treated as an unknown. Vibroseis data with a minimum frequency of 1 Hz were sensed along a horizontal surface trench as part of a baseline seismic survey carried out by the University of Calgary at the Containment and Monitoring Institute Field Research Station in Newell County, Alberta, Canada. These surface DAS data readily permit the picking of multimode dispersion curves, which are observed to enhance velocity profile resolution in both shallow and deep regions of the near-surface simultaneously, with the TD algorithm adapting the model to reflect this improved resolution. To avoid collecting abnormal model samples with thin-interleaved high- and low-velocity layers based on the known geological information of the field site, we employed constraints that preclude the structures that have velocity drops over 100 m s−1 along depth. Data errors are estimated via a non-parametric iterative process in terms of covariance matrices that include off-diagonal elements. Synthetic examples show that SWD with higher-order modes provides additional constraints on the structure and accurate noise estimation. Inversion of the field data resulted in high-resolution estimates of shear wave velocity as a function of depth throughout the top 120 m of the subsurface. The inferred structure is consistent with existing estimates of the regional lithology but resolves additional layers between 1- and 50-m depth.

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study.

Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law's mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R2\geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R2\geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for in-vivo signals.

Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms

In recent decades, various applications of shear wave velocity have been reported in oil and gas projects. These applications include the determination of lithology, estimation of geomechanical parameters, reservoir fluid detection, etc. The shear wave velocity is usually measured or recorded by DSI (Dipole Shear Sonic imager) instruments. This log is not usually available in all wells of a hydrocarbon field because of the high cost of running this log compared to other conventional logs. However, it is tried to estimate the shear wave velocity from other related logs.The literature contains many empirical rock physics models used to estimate shear wave velocity. Most of them were developed for sandstone lithologies and didn't applicable in carbonate reservoir because of existing some vertical and horizontally complex fracture networks in the carbonate reservoirs that cause a quite different behavior for the shear wave velocity.The main objective of this research is to concentrate on the effect of lithology in the estimation of the target log for a carbonate reservoir. At the earliest step, preprocessing is performed and 455 data points of several conventional well logs, i.g., acoustic, density, neutron porosity, resistivity, gamma-ray, calcite volume, dolomite volume, and water saturation logs have been applied to generate the synthetic shear wave velocity log in a carbonate oil reservoir.First, original data are divided into training, validation, and testing subsets. Different data-driven predictive models were built: Multiple Linear Regression (MLR), Ensemble Learning Method (ELM), and three architectures of Artificial Neural Networks (ANN). The performance of all techniques was compared with each other. Finally, the feed-forward neural network shows the highest accuracy with R-values of 0.99 and 0.96 for the training and testing datasets. Moreover, the network design and tuning parameters of the model are adjusted using the grid search optimization technique to obtain the optimal design of the artificial neural network for estimating the shear wave velocity log. Hence, a deep artificial neural network, i.e., [8–11–15-1] network with bayesian regularization training algorithm and hyperbolic tangent sigmoid transfer function, is proposed to predict the target response in other wells.

Shear wave velocity estimation in the Bengal Basin, Bangladesh by HVSR analysis: implications for engineering bedrock depth

The S-wave velocity (VS) in the Bengal Basin, Bangladesh has not been resolved from the ground surface to an intermediate depth in a regional context despite its importance for seismic hazard and risk evaluation. For this reason, we estimated VS profiles beneath 19 seismic stations in Bangladesh to a depth approximately 2800 m by employing full horizontal to vertical spectral ratio (HVSR) curve inversion under the diffuse field theory for the noise wavefield. The seismic stations are concentrated in three tectonic zones within the basin: the Surma basin (SB, Zone 1), Bengal Foredeep (BF, Zone 2), and Chittagong Tripura Fold Belt (CTFB, Zone 3). Full HVSR analysis (from 0.2 to 10 Hz) allowed us to obtain deep profiles with combined insights from shallow geotechnical boreholes and deep P-wave velocity (VP) information from active seismic surveys. From the resultant VS profiles, engineering bedrock (VS > 760 m/s) depths were also identified throughout the study area for the first time. The VS profiles within the Holocene to Miocene sedimentary sequences showed rapid variations from location to location. This is due to the highly variable near-surface geology caused by the dense and complex river network and tectonic deformation in Bangladesh. Except for three stations, the engineering bedrock depth exceeded 30 m at all stations. These results indicate the existence of deep soft soil in the study area, where VS30 based site characterization is inappropriate. Furthermore, seismic site response was estimated at a station (DHAK) by simulating a subduction zone earthquake. The resulting response spectrum (RS) exhibited ground motion amplification over a longer period, suggesting that multistory buildings at the site may be at risk if subjected to large earthquakes. The outcomes of this study can serve as useful guidelines for seismic risk reduction planning in Bangladesh.

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure-strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure-strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure-strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure-strain loop of central arteries while observing pressure signals from peripheral arteries.

Efficacy of ultrasound vector flow imaging in tracking omnidirectional pulsatile flow.

Ultrasound vector flow imaging (VFI) shows potential as an emerging non-invasive modality for time-resolved flow mapping. However, its efficacy in tracking multidirectional pulsatile flow with temporal resolvability has not yet been systematically evaluated because of the lack of an appropriate test protocol. We present the first systematic performance investigation of VFI in tracking pulsatile flow in a meticulously designed scenario with time-varying, omnidirectional flow fields (with flow angles from 0° to 360°). Ultrasound VFI was performed on a three-loop spiral flow phantom (4mm diameter; 5mm pitch) that was configured to operate under pulsatile flow conditions (10ml/s peak flow rate; 1Hz pulse rate; carotid pulse shape). The spiral lumen geometry was designed to simulate recirculatory flow dynamics observed in the heart and in curvy blood vessel segments such as the carotid bulb. The imaging sequence was based on steered plane wave pulsing (-10°, 0°, +10° steering angles; 5MHz imaging frequency; 3.3kHz interleaved pulse repetition frequency). VFI's pulsatile flow estimation performance and its ability to detect secondary flow were comparatively assessed against flow fields derived from computational fluid dynamics (CFD) simulations that included consideration of fluid-structure interactions (FSI). The mean percentage error (MPE) and the coefficient of determination (R2 ) were computed to assess the correspondence of the velocity estimates derived from VFI and CFD-FSI simulations. In addition, VFI's efficacy in tracking pulse waves was analyzed with respect to pressure transducer measurements made at the phantom's inlet and outlet. Pulsatile flow patterns rendered by VFI agreed with the flow profiles computed from CFD-FSI simulations (average MPE: -5.3%). The shape of the VFI-measured velocity magnitude profile generally matched the inlet flow profile. High correlation exists between VFI measurements and simulated flow vectors (lateral velocity: R2 =0.8; axial velocity R2 =0.89; beam-flow angle: R2 =0.98; p<0.0001 for all three quantities). VFI was found to be capable of consistently tracking secondary flow. It also yielded pulse wave velocity (PWV) estimates (5.72±1.02m/s) that, on average, are within 6.4% of those obtained from pressure transducer measurements (6.11±1.15m/s). VFI can consistently track omnidirectional pulsatile flow on a time-resolved basis. This systematic investigation serves well as a quality assurance test of VFI.

In Vivo Comparison of Pulse Wave Velocity Estimation Based on Ultrafast Plane Wave Imaging and High-Frame-Rate Focused Transmissions.

Ultrasound-based local pulse wave velocity (PWV) estimation, as a measure of arterial stiffness, can be based on fast focused imaging (FFI) or plane wave imaging (PWI). This study was aimed at comparing the accuracy of in vivo PWV estimation using FFI and PWI. Ultrasound radiofrequency data of carotid arteries were acquired in 14 healthy volunteers (25-57 y) by executing the FFI (12 lines, 7200 Hz) and PWI (128 lines, 2000 Hz) methods consecutively. PWV was derived at two time-reference points, dicrotic notch (DN) and systolic foot (SF), for multiple pressure cycles by fitting a linear function through the positions of the peaks of low-pass filtered wall acceleration curves as a function of time. The accuracy of PWV estimation was determined for various cutoff frequencies (10-200 Hz). No statistically significant difference was observed between PWVs estimated by both approaches. The PWV and R2 at DN were higher, on average, than those at SF (PWV/R2: FFI SF 5.5/0.92, FFI DN 6.1/0.92; PWI SF 5.4/0.89, PWI DN 6.3/0.95). The use of cutoff frequencies between 40 and 80Hz provided the most accurate PWVs. Both methods seemed equally suitable for use in clinical practice, although we have a preference for the PWV at DN given the higher R2 values.

Impact of estimated pulse wave velocity on cardiovascular outcomes in a general population

Abstract Background Estimated pulse wave velocity (ePWV), a simple surrogate estimate of carotid-femoral pulse wave velocity which was gold standard for measuring aortic stiffness, has been demonstrated to predict cardiovascular (CV) outcome. However, there was limited data on its predictive capacity for CV outcomes in the general population. The study aim was to investigate the independent association between ePWV and CV outcomes in general population. Methods A total of 10,030 subjects aged between 40 and 69 years were followed over 18-year period in the Ansan-Ansung cohort study. Levels of ePWV, which was calculated from an equation based on age and mean blood pressure, were categorized according to quartiles. A major adverse cardiovascular event (MACE) was defined as a composite of myocardial infarction, coronary artery disease, stroke, heart failure, peripheral artery disease, and cardiovascular death. Results The incidence rates of CV death, and MACE were 7.0% and 22.1% in the fourth (highest) quartile of ePWV and 0.1% and 4.5% in the first (lowest) quartile of ePWV. After adjusting for relevant covariates, patients with third and fourth quartile of ePWV showed significantly higher risk of CV death (hazard ratio [HR] 3.92; 95% confidence intervals [CI] 1.09–14.15 in third quartile and HR 8.53; 95% CI 2.13–34.10 in fourth quartile), and MACE (HR 1.54; 95% CI 1.15–2.08 in third quartile and HR 1.56; 95% CI 1.05–2.32) compared to the reference of first (lowest) quartile of ePWV. However, there were no improvement of C statistic for CV death and MACE when adding ePWV to the Cox regression models with 10-year atherosclerotic cardiovascular disease (ASCVD) risk. Conclusion These results suggest that ePWV predicted CV death and MACE in general population. The value of ePWV, a simple and useful indicator of aortic stiffness, is expected to serve as a potential marker to identify high risk groups of CV event in general population. Funding Acknowledgement Type of funding sources: None.

Assessment of rock geomechanical properties and estimation of wave velocities

Assessment of rock geomechanical properties and estimation of wave velocities