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.

Here we propose an ultrasound contrast-based imaging method that enables non-invasive quantitative assessment of ambient pressure changes inside the body (such as blood pressure). We subject the microbubbles in the contrast agent to two frequencies: A low-frequency (57 kHz) signal that dynamically manipulates the ambient pressure, and a series of high-frequency (4 MHz) pulses for exciting and imaging the bubble response. The imaging pulses exploit the ambient pressure sensitivity of the subharmonic microbubble response, while the low-frequency signal provided an intrinsic calibration for measurement of ambient pressure changes. We tested this approach in an in vitro setup and show that it can visualize and quantify ambient pressure differences with a sensitivity of 0.5 dB/kPa.

Ultrasound contrast agents, comprised of phospholipid-coated microbubbles, can be produced as monodisperse populations using a microfluidic flow-focusing device. However, microbubble coalescence remains a significant challenge. High production temperatures (e.g., 55 °C) can be used to suppress coalescence, but it complicates the microfluidic device design and is incompatible with targeting agents and drug conjugates. This study investigates the production of monodisperse microbubbles at room temperature with the addition of the amphiphilic surfactant Pluronic F68. Two 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC)-based phospholipid formulations were investigated: F1, containing 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[carbonyl-methoxypolyethylene glycol] (DPPE-PEG5000), and F2, which included both DPPE-PEG5000 and polyoxyethylene(40) stearate (PEG40-stearate). We characterized the size stability and acoustic behavior of monodisperse microbubbles produced with various Pluronic F68 concentrations. Adding 5-10 mol % Pluronic F68 was found to effectively suppress coalescence and facilitated the production of monodisperse microbubbles that remained shelf stable for at least 7 days. Acoustic attenuation measurements revealed a shell stiffness ranging from 0.78 to 0.93 N/m for these microbubbles. The 10 mol % Pluronic F68 addition (10PF) demonstrated superior monodispersity and was selected for further experiments. Upon dilution, the size and resonance frequencies of both F1-10PF and F2-10PF decreased over time, though F2-10PF showed better stability compared to F1-10PF for both metrics. Both F1-10PF and F2-10PF exhibited a stronger subharmonic scattering intensity than SonoVue (clinical approved microbubbles), which offers potential for blood pressure sensing. Our study shows that incorporating Pluronic F68 facilitates the production of monodisperse microbubbles at room temperature that are stable long-term and have excellent acoustical properties, with the F2-10PF formulation demonstrating better stability than the F1-10PF.

IN [1], there is a mistake in the timing diagram shown in Fig. 6. Switches S 1-S 4 are skipping some of the samples and the rate at which they are operating implies a TDM rate of 10 MHz, whereas (as described in [1]) this should be 20 MHz. In the updated Fig. 6, S 1-S 4 have been updated and a minor change has been made to the timing shown for switches Q₁ and Q₂, such that the correct TDM rate is indicated and no sample provided to the S/H stage via N1-N4 is skipped in the diagram. (Figure presented).

Objective: Described here is the development of an ultrasound matrix transducer prototype for high-frame-rate 3-D intra-cardiac echocardiography. Methods: The matrix array consists of 16 × 18 lead zirconate titanate elements with a pitch of 160 µm × 160 µm built on top of an application-specific integrated circuit that generates transmission signals and digitizes the received signals. To reduce the number of cables in the catheter to a feasible number, we implement subarray beamforming and digitization in receive and use a combination of time-division multiplexing and pulse amplitude modulation data transmission, achieving an 18-fold reduction. The proposed imaging scheme employs seven fan-shaped diverging transmit beams operating at a pulse repetition frequency of 7.7 kHz to obtain a high frame rate. The performance of the prototype is characterized, and its functionality is fully verified. Results: The transducer exhibits a transmit efficiency of 28 Pa/V at 5 cm per element and a bandwidth of 60% in transmission. In receive, a dynamic range of 80 dB is measured with a minimum detectable pressure of 10 Pa per element. The element yield of the prototype is 98%, indicating the efficacy of the manufacturing process. The transducer is capable of imaging at a frame rate of up to 1000 volumes/s and is intended to cover a volume of 70° × 70° × 10 cm. Conclusion: These advanced imaging capabilities have the potential to support complex interventional procedures and enable full-volumetric flow, tissue, and electromechanical wave tracking in the heart.

This article presents an application-specific integrated circuit (ASIC) for catheter-based 3-D ultrasound imaging probes. The pitch-matched design implements a comprehensive architecture with high-voltage (HV) transmitters, analog front ends, hybrid beamforming analog-To-digital converters (ADCs), and data transmission to the imaging system. To reduce the number of cables in the catheter while maintaining a small footprint per element, transmission (TX) beamforming is realized on the chip with a combination of a shift register (SR) and a row/column (R/C) approach. To explore an additional cable-count reduction in the receiver part of the design, a channel with a combination of time-division multiplexing (TDM), subarray beamforming, and multi-level pulse amplitude modulation (PAM) data transmission is also included. This achieves an 18-fold cable-count reduction and minimizes the power consumption in the catheter by a load modulation (LM) cable driver. It is further explored how common-mode interference can limit beamforming gain and a strategy to reduce its impact with local regulators is discussed. The chip was fabricated in TSMC 0.18-m HV BCD technology and a 2-D PZT transducer matrix of 16 × 18 elements with a pitch of 160 m and a center frequency of 6 MHz was manufactured on the chip. The system can generate all required TX patterns at up to 30 V, provides quick settling after the TX phase, and has an reception (RX) power consumption of only 1.12 mW/element. The functionality and operation of up to 1000 volumes/s have been demonstrated in electrical and acoustic imaging experiments.

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.

Objective: Post-operative brain injury in neonates may result from disturbed cerebral perfusion, but accurate peri-operative monitoring is lacking. High-frame-rate (HFR) cerebral ultrasound could visualize and quantify flow in all detectable vessels using spectral Doppler; however, automated quantification in small vessels is challenging because of low signal amplitude. We have developed an automatic envelope detection algorithm for HFR pulsed wave spectral Doppler signals, enabling neonatal brain quantitative parameter maps during and after surgery. Methods: HFR ultrasound data from high-risk neonatal surgeries were recorded with a custom HFR mode (frame rate = 1000 Hz) on a Zonare ZS3 system. A pulsed wave Doppler spectrogram was calculated for each pixel containing blood flow in the image, and spectral peak velocity was tracked using a max-likelihood estimation algorithm of signal and noise regions in the spectrogram, where the most likely cross-over point marks the blood flow velocity. The resulting peak systolic velocity (PSV), end-diastolic velocity (EDV) and resistivity index (RI) were compared with other detection schemes, manual tracking and RIs from regular pulsed wave Doppler measurements in 10 neonates. Results: Envelope detection was successful in both high- and low-quality arterial and venous flow spectrograms. Our technique had the lowest root mean square error for EDV, PSV and RI (0.46 cm/s, 0.53 cm/s and 0.15, respectively) when compared with manual tracking. There was good agreement between the clinical pulsed wave Doppler RI and HFR measurement with a mean difference of 0.07. Conclusion: The max-likelihood algorithm is a promising approach to accurate, automated cerebral blood flow monitoring with HFR imaging in neonates.

The accurate determination of the transfer function of ultrasound transducers is important for their design and operational performance. However, conventional methods for quantifying the transfer function, such as hydrophone measurements, radiation force balance, and pulse-echo measurements, are costly and complex due to specialized equipment required. In this study, we introduce a novel approach to estimate the transfer function of ultrasound transducers by measuring the acoustic streaming velocity generated by the transducer. We utilize an experimental setup consisting of a water tank with a millimeter scale, an ink-filled syringe, and a camera for recording the streaming phenomenon. Through streaming velocity measurements in the frequency range from 2 to 8 MHz, we determined the transfer function of an unfocused circular transducer with a center frequency of 5 MHz and a radius of 5.6 mm. We compared the performance of our method with hydrophone and pulse-echo measurements. At the center frequency, we measured a transmit efficiency of 1.9 kPa/V using the streaming approach, while hydrophone and pulse-echo measurements yielded transmit efficiencies of 2.1 kPa/V and 1.8 kPa/V, respectively. These findings demonstrate that the proposed method for estimating the transfer function of ultrasound transducers achieves a sufficient level of accuracy comparable to pulse-echo and hydrophone measurements.

Assessing the coronary circulation with contrast-enhanced echocardiography has high clinical relevance. However, it is not being routinely performed in clinical practice because the current clinical tools generally cannot provide adequate image quality. The contrast agent's visibility in the myocardium is generally poor, impaired by motion and nonlinear propagation artifacts. The established multipulse contrast schemes (MPCSs) and the more experimental singular value decomposition (SVD) filter also fall short to solve these issues. Here, we propose a scheme to process amplitude modulation/amplitude-modulated pulse inversion (AM/AMPI) echoes with higher order SVD (HOSVD) instead of conventionally summing the complementary pulses. The echoes from the complementary pulses form a separate dimension in the HOSVD algorithm. Then, removing the ranks in that dimension with dominant coherent signals coming from tissue scattering would provide the contrast detection. We performed both in vitro and in vivo experiments to assess the performance of our proposed method in comparison with the current standard methods. A flow phantom study shows that HOSVD on AM pulsing exceeds the contrast-to-background ratio (CBR) of conventional AM and an SVD filter by 10 and 14 dB, respectively. In vivo porcine heart results also demonstrate that, compared to AM, HOSVD improves CBR in open-chest acquisition (up to 19 dB) and contrast ratio (CR) in closed-chest acquisition (3 dB).

This paper presents a pitch-matched transceiver ASIC integrated with a 2-D transducer array for a wearable ultrasound device for transfontanelle ultrasonography. The ASIC combines 8-fold multiplexing, 4-channel micro-beamforming (μ BF) and sub-array-level digitization to achieve a 128-fold channel-count reduction. The μ BF is based on passive boxcar integration and interfaces with a 10-bit 40 MS/s SAR ADC in the charge domain, thus obviating the need for explicit anti-alias filtering 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 concatenates outputs of four ADCs, leading to an aggregate 3.84 Gb/s data rate. Per channel, the RX circuit consumes 2.06 mW and occupies 0.05 mm2.

There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count to acceptable levels, receive processing methods that try to improve spatial resolution will have to compensate the introduced reduction in focusing. Existing beamformers do not realize sufficient improvement and/or have a computational cost that prohibits their use. Here we propose the use of adaptive beamforming by deep learning (ABLE) in combination with training targets generated by a large aperture array, which inherently has better lateral resolution. In addition, we modify ABLE to extend its receptive field across multiple voxels. We illustrate that this method improves lateral resolution both quantitatively and qualitatively, such that image quality is improved compared with that achieved by existing delay-and-sum, coherence factor, filtered-delay-multiplication-and-sum and Eigen-based minimum variance beamformers. We found that only in silica data are required to train the network, making the method easily implementable in practice.

Ultrasound-based shear wave elastography is a promising technique to non-invasively assess the dynamic stiffness variations of the heart. The technique is based on tracking the propagation of acoustically induced shear waves in the myocardium of which the propagation speed is linked to tissue stiffness. This measurement is repeated multiple times across the cardiac cycle to assess the natural variations in wave propagation speed. The interpretation of these measurements remains however complex, as factors such as loading and contractility affect wave propagation. We therefore applied transthoracic shear wave elastography in 13 pigs to investigate the dependencies of wave speed on pressure–volume derived indices of loading, myocardial stiffness, and contractility, while altering loading and inducing myocardial ischemia/reperfusion injury. Our results show that diastolic wave speed correlates to a pressure–volume derived index of operational myocardial stiffness (R = 0.75, p < 0.001), suggesting that both loading and intrinsic properties can affect diastolic wave speed. Additionally, the wave speed ratio, i.e. the ratio of systolic and diastolic speed, correlates to a pressure–volume derived index of contractility, i.e. preload-recruitable stroke work (R = 0.67, p < 0.001). Measuring wave speed ratio might thus provide a non-invasive index of contractility during ischemia/reperfusion injury.