Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-023-44588-4, published online 17 October 2023 The original version of this Article contained errors. An error in the pulse repetition frequency (PRF) of the imaging sequence was discovered during subsequent analysis of the data. Acoustic verification of the original acquisition script confirmed the incorrect PRF. Although the relative comparisons in the study remain valid because the PRF error was the same across all conditions, the absolute wave speed values require correction with a factor 0.901 to ensure scientific accuracy. As the result, in Materials and methods section, under ‘Shear wave elastography’ subheading, “One SWE sequence consisted of 1.5 - 2 s recording time in which multiple individual SWE acquisitions were performed at intervals of 28 ms (34 SWE acquisitions per second), as illustrated in the first row of Fig. 2.” now reads: “One SWE sequence consisted of 1.7 - 2.2 s recording time in which multiple individual SWE acquisitions were performed at intervals of 32 ms (31 SWE acquisitions per second), as illustrated in the first row of Fig. 2.” “The resulting shear wave propagation was consecutively recorded at a minimal frame rate of 6.2 kHz using diverging wave imaging.” now reads: “The resulting shear wave propagation was consecutively recorded at a minimal frame rate of 5.6 kHz using diverging wave imaging.” As the result, Figure 2 and its legend were incorrect. The original Figure 2 and accompanying legend appear below. (Figure presented.) Shear wave elastography (SWE) sequence and postprocessing workflow. First row: schematic of a SWE imaging sequence, consisting of SWE acquisitions that were taken at 34 Hz during 1.5–2 s. Second row: schematic of an ECG signal. Third row: tissue velocity panels along the septum for different acquisitions at different time points, together with shear wave speed estimation. Fourth row: Shear wave speed data for three different SWE sequences at one intervention stage. The last panel demonstrates the procedure of obtaining diastolic and systolic shear wave propagation speed (SWS_dia and SWS_sys) via piecewise linear model fitting. Different colors of grey represent different heartbeats. Spread of estimated wave speed at one time point represents variability across 10 anatomical M-lines drawn by the 2 observers. Figure 2 legend, “Shear wave elastography (SWE) sequence and postprocessing workflow. First row: schematic of a SWE imaging sequence, consisting of SWE acquisitions that were taken at 34 Hz during 1.5–2 s. Second row: schematic of an ECG signal. Third row: tissue velocity panels along the septum for different acquisitions at different time points, together with shear wave speed estimation. Fourth row: Shear wave speed data for three different SWE sequences at one intervention stage. The last panel demonstrates the procedure of obtaining diastolic and systolic shear wave propagation speed (SWS_dia and SWS_sys) via piecewise linear model fitting. Different colors of grey represent different heartbeats. Spread of estimated wave speed at one time point represents variability across 10 anatomical M-lines drawn by the 2 observers.” now reads: “Shear wave elastography (SWE) sequence and postprocessing workflow. First row: schematic of a SWE imaging sequence, consisting of SWE acquisitions that were taken at 31 Hz during 1.7-2.2 s. Second row: schematic of an ECG signal. Third row: tissue velocity panels along the septum for different acquisitions at different time points, together with shear wave speed estimation. Fourth row: Shear wave speed data for three different SWE sequences at one intervention stage. The last panel demonstrates the procedure of obtaining diastolic and systolic shear wave propagation speed (SWSdia and SWSsys) via piecewise linear model fitting. Different colors of grey represent different heartbeats. Spread of estimated wave speed at one time point represents variability across 10 anatomical M-lines drawn by the 2 observers.” In the Results section, under the subheading ‘Diastolic wave speed’, “The diastolic wave speed for the different interventions is summarized in Fig. 5a, with a wave speed of 1.3 m/s in baseline. Significant changes in wave speed were only observed after ischemia injury (+ 57%), whereas other interventions did not significantly alter the wave speed (− 18% in preload decrease, + 5% in afterload increase, + 4% in preload increase and + 94% after reperfusion).” now reads: “The diastolic wave speed for the different interventions is summarized in Figure 5a, with a wave speed of 1.2 m/s in baseline. Significant changes in wave speed were only observed after ischemia injury (+57%), whereas other interventions did not significantly alter the wave speed (-17% in preload decrease, +5% in afterload increase, +4% in preload increase and +94% after reperfusion).” “A similar correlation is found between SWS and operational chamber stiffness dP/dV (R = 0.57; p < 0.01 in Fig. 6b). SWE measurements during and after I/R injury—as depicted in orange in Fig. 6—showed a strong significant correlation to EDP (R = 0.68; p < 0.01), operational chamber stiffness dP/dV (R = 0.73; p < 0.01) and stiffness constant β (R = 0.50; p = 0.03). Diastolic wave speed is more sensitive to changes in intrinsic stiffness than in loading, as reflected by the larger slope of the regression line (0.8 vs. 0.35 in Fig. 6b).” now reads: “A similar correlation is found between SWS and operational chamber stiffness dP/dV (R=0.54; p<0.01 in Figure 6b). SWE measurements during and after I/R injury – as depicted in orange in Figure 6 – showed a strong significant correlation to EDP (R=0.68; p<0.01), operational chamber stiffness dP/dV (R=0.73; p<0.01) and stiffness constant β (R=0.50; p=0.036). Diastolic wave speed is more sensitive to changes in intrinsic stiffness than in loading, as reflected by the larger slope of the regression line (0.73 vs. 0.29 in Figure 6b).” As the result, Figures 4, 5, 6 and 8 were incorrect. The original Figures 4, 5, 6 and 8 and accompanying legends appear below. (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) Variability of shear wave speed (SWS) estimation. (a) Example of low variability and good fit. (b) Example of high variability and moderate fit. (c) Averaged relative wave speed deviation from fit for all pigs is depicted for each condition and diastole/systole. Diastolic and systolic wave speeds for the different interventions: baseline (BL), preload decrease (PD), afterload increase (AI), preload increase (PI), myocardial ischemia (MI) and reperfusion (RP). *p < 0.05 for t-test with Bonferroni correction. Correlations of diastolic and systolic wave speed, with (a) end-diastolic pressure (EDP), (b) operational stiffness (dP/dV), (c) stiffness constant β, (d) end-systolic pressure (ESP) and (e) preload-recruitable stroke work (PRSW) during loading (blue) and stiffness interventions (orange). Correlation between diastolic wave speed and operational chamber stiffness (dP/dV) and between wave speed ratio and preload-recruitable stroke work (PRSW) for all interventions. Additionally, Table 2 was incorrect. The original Table 2 and accompanying legends appear below. (Table presented.) Linear regression results between diastolic wave speed (SWS_dia) and end-diastolic pressure (EDP) on one hand and systolic wave speed (SWS_sys) and end-systolic pressure (ESP) on the other hand during loading interventions, with goodness of fit R². Pig # SWS_dia vs. EDP SWS_sys vs. ESP Slope (m/s/mmHg) Intercept (m/s) R² Slope (m/s/mmHg) Intercept (m/s) R² 1 0.011 1.1 0.92 0.019 1.6 0.93 2 0.026 1.3 0.92 0.017 1.7 0.99 3 0.016 1.1 0.93 0.024 1.4 0.95 4 0.021 1.1 0.53 0.026 1.7 0.997 5 0.018 1.3 0.60 0.012 2.8 0.85 6 0.014 1.3 0.81 0.024 1.6 0.91 7 0.014 1.2 0.70 0.019 2.2 0.94 Mean 0.017 ± 0.005 1.2 ± 0.1 0.77 ± 0.17 0.020 ± 0.005 1.9 ± 0.5 0.94 ± 0.05 Under the subheading ‘Systolic wave speed’, “Figure 5b shows the resulting systolic wave speed for all interventions, with a wave speed of 3.9 m/s at baseline. The individual correlations with ESP are given in Table 2, and show in general a slightly higher goodness-of-fit (R²) than for the diastolic measurements, probably due to the limited sensitivity of the manual wave speed estimator to detect differences in speed (ΔSWS_dia = 0.37 m/s vs. ΔSWS_sys = 1.27 m/s).” now reads: “Figure 5b shows the resulting systolic wave speed for all interventions, with a wave speed of 3.5 m/s at baseline. The individual correlations with ESP are given in Table 2, and show in general a slightly higher goodness-of-fit (R²) than for the diastolic measurements, probably due to the limited sensitivity of the manual wave speed estimator to detect differences in speed (ΔSWS_dia = 0.33 m/s vs. ΔSWS_sys = 1.14 m/s).” In the Discussion section, under the subheading ‘Myocardial operational stiffness’, “Our study confirms this earlier research: the change in the stiffness constant β of the EDPVR increased after the ischemia period (0.073 vs. 0.045 1/ml; p = 0.06) and increased even further after the reperfusion period (0.087 vs. 0.045 1/ml; p < 0.05), which was reflected in the change of diastolic wave speed after ischemia injury (2.0 vs. 1.3 m/s; p < 0.05) and reperfusion injury (2.5 m/s). The difference in the slopes of the fitted linear regression curves in Fig. 6b (0.80 vs. 0.35) suggests that diastolic speed is more sensitive to changes in intrinsic characteristics than changes in loading.” now reads: “Our study confirms this earlier research: the change in the stiffness constant β of the EDPVR increased after the ischemia period (0.073 vs. 0.045 1/ml; p = 0.06) and increased even further after the reperfusion period (0.087 vs. 0.045 1/ml; p < 0.05), which was reflected in the change of diastolic wave speed after ischemia injury (1.8 vs. 1.2 m/s; p < 0.05) and reperfusion injury (2.3 m/s). The difference in the slopes of the fitted linear regression curves in Figure 6b (0.73 vs. 0.29) suggests that diastolic speed is more sensitive to changes in intrinsic characteristics than changes in loading.” Under the subheading ‘Contractility’, “However, systolic wave speed increased significantly after ischemia injury (4.9 vs. 3.9 m/s; p = 0.01 in Fig. 6c), which does not correspond with the observed decline in contractility in terms of pressure–volume measures after the I/R injury (see Table 1).” now reads: “However, systolic wave speed increased significantly after ischemia injury (4.4 vs. 3.5 m/s; p=0.01 in Figure 5b), which does not correspond with the observed decline in contractility in terms of pressure-volume measures after the I/R injury (see Table 1).” Further, in the original version of this Article Jürgen Duchenne was incorrectly affiliated with Affiliation 4. Their correct affiliation is Affiliation 5: 5. Cardiology, KU Leuven, Leuven, Belgium Finally, the Funding section was incomplete, “This work was supported by the Research Foundation Flanders (FWO, Brussels, Belgium) under Grant 1211620N to Annette Caenen and, Grants G092318N and 1832922N to Jens-Uwe Voigt. This work is also part of the TTW–Dutch Heart Foundation partnership program ‘Earlier recognition of cardiovascular diseases’ with project number 14740.” now reads: "This work was supported by the Research Foundation Flanders (FWO, Brussels, Belgium) under Grant 1211620N to Annette Caenen and, Grants G092318N and 1832922N to Jens-Uwe Voigt and Grant 12ZZN22N to Jürgen Duchenne. This work is also part of the TTW–Dutch Heart Foundation partnership program ‘Earlier recognition of cardiovascular diseases’ with project number 14740." The original Article has been corrected.

Ultrafast imaging, which uses unfocussed transmissions to form images, provides very high frame rates at the cost of low signal-to-noise ratio (SNR). This loss of SNR becomes especially apparent when imaging deeper structures. Ultrafast imaging is mostly used in combination with Doppler processing. Even if we apply tissue-separation filters, they lead to significant energy loss and decrease the SNR. Previous work showed that this loss in SNR and, hence, penetration depth can be partially regained using coded transmissions. However, these codes are mostly either standard or randomly generated and can be improved with a design rooted in an optimization scheme. To address this limitation, we design an optimized code tailored to ultrasound imaging with unfocused transmissions represented by a generalized encoding matrix in a linear signal model. We employ the minimization of the Cramér-Rao lower bound (CRB) over the unknown coding matrix as a way to optimize the code. Due to the high computational cost of the resulting optimization problems, we also introduce a trace-constraint optimization problem based on the Fisher information matrix (FIM). Simulation results show that the optimized code provides higher SNR in deep image regions than previously tested coding schemes such as the Barker code, albeit with a trade-off for decreased resolution. On the other hand, the application of least-squares QR (LSQR) mitigates this resolution degradation. Lastly, the optimized code was tested in simulations using a numerical model of a clinical transducer setting, demonstrating its potential for higher SNR in ultrafast Doppler imaging.

This article presents a pitch-matched transceiver application-specific integrated circuit (ASIC) for a wearable ultrasound device intended for transfontanelle ultrasonography, which includes element-level 20-V unipolar pulsers with transmit (TX) beamforming, and receive (RX) circuitry that combines eightfold multiplexing, four-channel micro-beamforming (?BF), and subgroup-level digitization to achieve an initial 32-fold channel-count reduction. The ?BF is based on passive boxcar integration, merged with a 10-bit 40 MS/s SAR ADC in the charge domain, thus obviating the need for explicit anti-alias filtering (AAF) and power-hungry ADC drivers. A compact and low-power reference generator employs an area-efficient MOS capacitor as a reservoir to quickly set a reference for the ADC in the charge domain. A low-power multi-level data link, based on 16-level pulse-amplitude modulation, concatenates the outputs of four ADCs, providing an overall 128-fold channel-count reduction. A prototype transceiver ASIC was fabricated in a 180-nm BCD technology, and interfaces with a 2-D PZT transducer array of 16 × 16 elements with a pitch of 125 ?m and a center frequency of 9 MHz. The ASIC consumes 1.83 mW/element. The data link achieves an aggregate 3.84 Gb/s data rate with 3.3 pJ/bit energy efficiency. The ASIC's functionality has been demonstrated through electrical, acoustic, and imaging experiments.

We apply a high frame rate (over 500 Hz) tissue Doppler method to measure the propagation velocity of naturally occurring shear waves (SW) generated by aortic and mitral valves closure. The aim of this work is to demonstrate clinical relevance. We included 45 healthy volunteers and 43 patients with hypertrophic cardiomyopathy (HCM). The mitral SW (4.68 ± 0.66 m/s) was consistently faster than the aortic (3.51 ± 0.38 m/s) in all volunteers (p < 0.0001). In HCM patients, SW velocity correlated with E/e’ ratio (r = 0.346, p = 0.04 for aortic SW and r = 0.667, p = 0.04 for mitral SW). A subgroup of 20 volunteers were matched for age and gender to 20 HCM patients. In HCM, the mean velocity of 5.1 ± 0.7 m/s for the aortic SW (3.61 ± 0.46 m/s in matched volunteers, p < 0.0001) and 6.88 ± 1.12 m/s for the mitral SW(4.65 ± 0.77 m/s in matched volunteers, p < 0.0001). A threshold of 4 m/s for the aortic SW correctly classified pathologic myocardium with a sensitivity of 95% and specificity of 90%. Naturally occurring SW can be used to assess differences between normal and pathologic myocardium.

This paper presents an ultrasound transceiver application-specific integrated circuit (ASIC) designed for 3-D ultrasonic imaging of the carotid artery. This application calls for an array of thousands of ultrasonic transducer elements, far exceeding the number of channels of conventional imaging systems. The 3.6 x 6.8 mm&#x00B2; ASIC interfaces a piezo-electric transducer (PZT) array of 24 x 40 elements, directly integrated on top of the ASIC, to an imaging system using only 24 transmit and receive channels. Multiple ASICs can be tiled together to form an even bigger array. The ASIC, implemented in a 0.18 &#x03BC;m high-voltage (HV) BCD process, consists of a reconfigurable switch matrix and row-level receive circuits. Each element is associated with a compact bootstrapped HV transmit switch, an isolation switch for the receive circuits and programmable logic that enables a variety of imaging modes. Electrical and acoustic experiments successfully demonstrate the functionality of the ASIC. In addition, the ASIC has been successfully used in a 3-D imaging experiment.

Introduction: To improve carotid artery stenting (CAS), more information about the functioning of the stent is needed. Therefore, a method that can image the flow near and around a stent is required. The aim of this study was to evaluate the performance of high-frame-rate contrast-enhanced ultrasound (HFR CEUS) in the presence of a stent. Methodology: HFR CEUS acquisitions of a carotid artery phantom, a silicone tube with diameter 8 mm, with and without a stent were acquired at transmit voltages of 2V, 4V and 10V using a Verasonics ultrasound system and C5-2 probe. Different concentrations of ultrasound contrast agent (UCA) were tested in a blood mimicking fluid (BMF). Particle image velocimetry (PIV) analysis was performed on Singular Value Decomposition (SVD) filtered images. Mean and peak velocities, and correlation coefficients were compared between stented and non-stented regions. Also, experimental results were compared with theoretical and numerical models. Results: The averaged experimental mean velocity (0.113 m/s) was significant lower than the theoretical and numerical mean velocity (0.129 m/s). The averaged experimental peak velocity (0.152 m/s) was significant lower than the theoretical and numerical peak velocity (0.259 m/s). Correlation coefficients and averaged mean velocity values were lower (difference of 0.022 m/s) in stented regions compared to non-stented regions. Conclusion: In vitro experiments showed an underestimation of mean and peak velocities in stented regions compared to non-stented regions. However, the microbubbles can be tracked efficiently and the expected laminar flow profile can be quantified using HFR CEUS near and around a stent.

Blood flow patterns in the human left ventricle (LV) have shown relation to cardiac health. However, most studies in the literature are limited to a few patients and results are hard to generalize. This study aims to provide a new framework to generate more generalized insights into LV blood flow as a function of changes in anatomy and wall motion. In this framework, we studied the four-dimensional blood flow in LV via computational fluid dynamics (CFD) in conjunction with a statistical shape model (SSM), built from segmented LV shapes of 150 subjects. We validated results in an in-vitro dynamic phantom via time-resolved optical particle image velocimetry (PIV) measurements. This combination of CFD and the SSM may be useful for systematically assessing blood flow patterns in the LV as a function of varying anatomy and has the potential to provide valuable data for diagnosis of LV functionality.

Generally, studies on structural design for bored tunnels focus on moderate to deep tunnels (cover-to-diameter ratio C/D ≥ 2). Such tunnel design methods cannot be used for shallow-situated bored tunnels because the influence of buoyancy is discounted, and actual loads on the tunnel lining are not taken into account properly. This paper proposes a new model that has more accurate loads on the tunnel lining combined with finite-element analysis for shallow tunnels. Internal forces and deformations of various shallow bored tunnels are investigated. The relationship between the optimal thickness-to-diameter ratio d/D of the tunnel cross section and the cover-to-diameter ratio C/D is also studied.

Recently, a new functional neuroimaging method called fUS was proposed, which is based on high-frame-rate Power Doppler imaging. So far, fUS has only been performed on rodents, but major issues in neuroscience such as cerebral asymmetries and language learning are mainly studied in birds. Here, we show the first successful fUS measurements on a non-mammalian species without cortical brain architecture, such as pigeons. These measurements are based on a framerate enhanced fUS acquisition algorithm, which was necessary to suppress the signal variations originating from the slower heartrate of pigeons.

This paper presents a front-end application-specific integrated circuit (ASIC) that demonstrates the feasibility of inprobe digitization for next-generation miniature 3-D ultrasound probes with acceptable power- and area-efficiency. The proposed design employs a low-power charge-domain ADC that is directly merged with the sample-and-hold delay lines in each subarray, and high-speed datalinks at the ASIC periphery to realize an additional channel-count reduction compared to prior work based on analog subarray beamforming. The 4.8 × 2 mm² ASIC, which has a compact layout element-matched to a 5-MHz 150-μm-pitch PZT matrix transducer, achieves an overall 36-fold channel-count reduction and a state-of-the-art power-efficiency with less than 1 mW/element power dissipation while receiving, which is acceptable even when scaled up to a 1000-element probe. The prototype ASIC has been fabricated in a 0.18 μm CMOS process. Its functionality has been successfully evaluated with both electrical and acoustical measurements.

Over the last decade, clinical studies show a strong interest in real-time 3D imaging. This calls for ultrasound probes with high-element-count 2D matrix transducer arrays. These may be interfaced to an imaging system using an in-probe Application Specific Integrated Circuit (ASIC) that takes care of signal amplification, element switching, sub-array beamforming, etc. Since the ASIC is made from silicon and is mounted directly behind the transducer elements, it can acoustically be regarded as a rigid plate that can sustain traveling lateral waves. These waves lead to acoustical cross-talk between the elements, and results in extra peaks in the directivity pattern. We propose two solutions to this problem, based on numerical simulations. One approach is to decrease the phase velocity in the silicon by reducing the silicon thickness and absorbing the energy using a proper backing material. Another solution is to disturb the waves inside the silicon plate by sub-dicing the back-side of the ASIC. We conclude that both solutions can be used to improve the directivity pattern.

Data acquisition from 2-D transducer arrays has become one of the main challenges for the development of endoscopic and catheter-based 3-D ultrasound imaging devices. Front-end ASICs with sub-array pre-beamforming have been reported that reduce the cable number by an order of magnitude. Further channel reduction requires digitization in the front-end ASIC to facilitate more in-probe data processing functions. Prior solutions, however, are too large and power-hungry to be integrated in a miniature ultrasound probe. In this work, we present a front-end ASIC with an element-pitch-matched layout that combines sub-array beamforming, digitization and high-speed data transmission. It achieves a 36-fold channel-count reduction and a record power-efficiency with less than 1 mW/element power dissipation in receive.

The size of the features, and their relative distance to the probe, vary a lot in the intracardiac echocardiography application thus challenging the design of the probe. Therefore it may be beneficial to design a versatile probe which can produce both a large image to provide overview for navigation, and a smaller but detailed anatomic image on the structures of interest. This could be achieved by a probe whose frequency range of operation can be tuned - on the fly - to the specific task. Our goal is to develop a forward-looking catheter which can change its imaging frequency in the range 5 MHz - 15 MHz, allowing for both high penetration and high resolution intracardiac imaging within a single device. Our design comprises a capacitive micromachined ultrasonic transducer (CMUT) array operated in collapse-mode, which allows tuning of the imaging frequency. Custom-made front-end electronics is integrated in a catheter tip close to the CMUT for improved performance. In this paper, we report on the frequency-agility of the fabricated collapse-mode 1-D CMUT array.

Ultrafast contrast enhanced ultrasound, combined with echo particle image velocimetry (ePIV), can provide accurate, multidimensional hemodynamic flow field measurement. However, the use of ultrasound contrast agent (UCA) still prevents this method from becoming a truly versatile and non-invasive diagnostic tool. In this study, we investigate the use of native blood instead of UCA backscatter for ePIV measurements and compare their accuracy in vitro. Additionally, the effect of measurement depth is experimentally assessed. Blood mimicking fluid (BMF) was pumped through a 10 mm diameter tube producing parabolic flow profiles, adding UCA in the case of contrast imaging. Plane wave imaging at 5000 framesper-second was performed with a Verasonics Vantage system and a linear array. The tube was imaged at three different depths: 25, 50 and 100 mm. Singular value decomposition (SVD) was assessed for clutter suppression against mean background subtraction. PIVlab was used as a PIV implementation. With SVD, BMF provided almost equal ePIV accuracy as UCA, except at 100 mm depth where UCA provided better accuracy. Use of clutter suppression greatly improved ePIV results, but minimal differences in ePIV accuracy were noted between mean and SVD filtered groups (BMF or UCA). Accuracy decreased with increasing depth, likely due to reduced elevation resolution, resulting in out-of-plane smoothing of velocity gradients.