In vivo demonstration of a real-time temporal SNR acoustic output adjustment method.

This work proposes a novel method of temporal signal-to-noise ratio (SNR) guided adaptive acoustic output adjustment and demonstrates this approach during in vivo fetal imaging. Acoustic output adjustment is currently the responsibility of sonographers, but ultrasound safety studies show recommended ALARA (As Low As Reasonably Achievable) practices are inconsistently followed. This study explores an automated ALARA method that adjusts the Mechanical Index (MI) output, targeting imaging conditions matching the temporal noise perception threshold. A 28 dB threshold SNR is used as the target SNR, following prior work showing relevant noise quantities are imperceptible once this image data quality level is reached. After implementing adaptive output adjustment on a clinical system, the average MI required to achieve 28 dB SNR in an eleven-volunteer fetal abdomen imaging test ranged from 0.17 to 0.26. The higher MI levels were required when imaging at higher frequencies. During tests with 20-second MI adjustment imaging periods, the degree of motion impacted the adaptive performance. For stationary imaging views, target SNR levels were maintained in 90% of SNR evaluations. When scanning between targets the imaging conditions were more variable, but the target SNR was still maintained in 71% of the evaluations. Given the relatively low MI recommended when performing MI adjustment and the successful adjustment of MI in response to changing imaging conditions, these results encourage adoption of adaptive acoustic output approaches guided by temporal SNR.

Human Observer Sensitivity to Temporal Noise During B-Mode Ultrasound Scanning: Characterization and Imaging Implications.

This work measures temporal signal-to-noise ratio (SNR) thresholds that indicate when random noise during ultrasound scanning becomes imperceptible to expert human observers. Visible noise compromises image quality and can potentially lead to non-diagnostic scans. Noise can arise from both stable acoustic sources (clutter) or randomly varying electronic sources (temporal noise). Extensive engineering effort has focused on decreasing noise in both of these categories. In this work, an observer study with five practicing sonographers was performed to assess sonographer sensitivity to temporal noise in ultrasound cine clips. Understanding the conditions where temporal noise is no longer visible during ultrasound imaging can inform engineering efforts seeking to minimize the impact this noise has on image quality. The sonographers were presented with paired temporal noise-free and noise-added simulated speckle cine clips and asked to select the noise-added clips. The degree of motion in the imaging target was found to have a significant effect on the SNR levels where noise was perceived, while changing imaging frequency had little impact. At realistic in vivo motion levels, temporal noise was not perceived in cine clips at and above 28 dB SNR. In a case study presented here, the potential of adaptive intensity adjustment based on this noise perception threshold is validated in a fetal imaging scenario. This study demonstrates how noise perception thresholds can be applied to help design or tune ultrasound systems for different imaging tasks and noise conditions.

Evaluation of Myocardial Stiffness in Cardiac Amyloidosis Using Acoustic Radiation Force Impulse and Natural Shear Wave Imaging.

OBJECTIVE: Increased myocardial stiffness (MS) is an important hallmark of cardiac amyloidosis (CA) caused by myocardial amyloid deposition. Standard echocardiography metrics assess MS indirectly via downstream effects of cardiac stiffening. The ultrasound elastography methods acoustic radiation force impulse (ARFI) and natural shear wave (NSW) imaging assess MS more directly. METHODS: This study compared MS in 12 healthy volunteers and 13 patients with confirmed CA using ARFI and NSW imaging. Parasternal long-axis acquisitions of the interventricular septum were obtained using a modified Acuson Sequoia scanner and a 5V1 transducer. ARFI-induced displacements were measured through the cardiac cycle, and ratios of diastolic-over-systolic displacement were calculated. NSW speeds from aortic valve closure were extracted from echocardiography-tracked displacement data. RESULTS: ARFI stiffness ratios were significantly lower in CA patients than controls (mean ± standard deviation: 1.47 ± 0.27 vs. 2.10 ± 0.47, p < 0.001), and NSW speeds were significantly higher in CA patients than controls (5.58 ± 1.10 m/s vs. 3.79 ± 1.10 m/s, p < 0.001). A linear combination of the two metrics exhibited greater diagnostic potential than either metric alone (area under the curve = 0.97 vs. 0.89 and 0.88). CONCLUSION: MS was measured to be significantly higher in CA patients using both ARFI and NSW imaging. Together, these methods have potential utility to aid in clinical diagnosis of diastolic dysfunction and infiltrative cardiomyopathies.

Large-Array Deep Abdominal Imaging in Fundamental and Harmonic Mode.

Deep abdominal images suffer from poor diffraction-limited lateral resolution. Extending the aperture size can improve resolution. However, phase distortion and clutter can limit the benefits of larger arrays. Previous studies have explored these effects using numerical simulations, multiple transducers, and mechanically swept arrays. In this work, we used an 8.8-cm linear array transducer to investigate the effects of aperture size when imaging through the abdominal wall. We acquired channel data in fundamental and harmonic modes using five aperture sizes. To avoid motion and increase the parameter sampling, we decoded the full-synthetic aperture data and retrospectively synthesized nine apertures (2.9-8.8 cm). We imaged a wire target and a phantom through ex vivo porcine abdominal samples and scanned the livers of 13 healthy subjects. We applied bulk sound speed correction to the wire target data. Although point resolution improved from 2.12 to 0.74 mm at 10.5 cm depth, contrast resolution often degraded with aperture size. In subjects, larger apertures resulted in an average maximum contrast degradation of 5.5 dB at 9-11 cm depth. However, larger apertures often led to visual detection of vascular targets unseen with conventional apertures. An average 3.7-dB contrast improvement over fundamental mode in subjects showed that the known benefits of tissue-harmonic imaging extend to larger arrays.

An Automated Region-Selection Method for Adaptive ALARA Ultrasound Imaging.

The objective of this work was to develop an automated region of the interest selection method to use for adaptive imaging. The as low as reasonably achievable (ALARA) principle is the recommended framework for setting the output level of diagnostic ultrasound devices, but studies suggest that it is not broadly observed. One way to address this would be to adjust output settings automatically based on image quality feedback, but a missing link is determining how and where to interrogate the image quality. This work provides a method of region of interest selection based on standard, envelope-detected image data that are readily available on ultrasound scanners. Image brightness, the standard deviation of the brightness values, the speckle signal-to-noise ratio, and frame-to-frame correlation were considered as image characteristics to serve as the basis for this selection method. Region selection with these filters was compared to results from image quality assessment at multiple acoustic output levels. After selecting the filter values based on data from 25 subjects, testing on ten reserved subjects' data produced a positive predictive value of 94% using image brightness, the speckle signal-to-noise ratio, and frame-to-frame correlation. The best case filter values for using only image brightness and speckle signal-to-noise ratio had a positive predictive value of 97%. These results suggest that these simple methods of filtering could select reliable regions of interest during live scanning to facilitate adaptive ALARA imaging.

Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST).

Multi-covariate imaging of sub-resolution targets (MIST) is a statistical, model-based image formation technique that smooths speckles and reduces clutter. MIST decomposes the measured covariance of the element signals into modeled contributions from mainlobe, sidelobes, and noise. MIST covariance models are derived from the well-known autocorrelation relationship between transmit apodization and backscatter covariance. During in vivo imaging, the effective transmit aperture often deviates from the applied apodization due to nonlinear propagation and wavefront aberration. Previously, the backscatter correlation length provided a first-order measure of these patient-specific effects. In this work, we generalize and extend this approach by developing data-adaptive covariance estimation, parameterization, and model-formation techniques. We performed MIST imaging using these adaptive models and evaluated the performance gains using 152 tissue-harmonic scans of fetal targets acquired from 15 healthy pregnant subjects. Compared to standard MIST imaging, the contrast-to-noise ratio (CNR) is improved by a median of 8.3%, and the speckle signal-to-noise ratio (SNR) is improved by a median of 9.7%. The median CNR and SNR gains over B-mode are improved from 29.4% to 40.4% and 24.7% to 38.3%, respectively. We present a versatile empirical function that can parameterize an arbitrary speckle covariance and estimate the effective coherent aperture size and higher order coherence loss. We studied the performance of the proposed methods as a function of input parameters. The implications of system-independent MIST implementation are discussed.

Reverberation Clutter Suppression Using 2-D Spatial Coherence Analysis.

Diffuse reverberation clutter often significantly degrades the visibility of abdominal structures. Reverberation clutter acts as a temporally stationary haze that originates from the multiple scattering within the subcutaneous layers and has a narrow spatial correlation length. We recently presented an adaptive beamforming technique, Lag-one Spatial Coherence Adaptive Normalization (LoSCAN), which can recover the contrast suppressed by incoherent noise. LoSCAN successfully suppressed reverberation clutter in numerous clinical examples. However, reverberation clutter is a 3-D phenomenon and can often exhibit a finite partial correlation between receive channels. Due to a strict noise-incoherence assumption, LoSCAN does not eliminate correlated reverberation clutter. This work presents a 2-D matrix array-based LoSCAN method and evaluates matrix-LoSCAN-based strategies to suppress partially correlated reverberation clutter. We validated the proposed matrix LoSCAN method using Field II simulations of a 64×64 symmetric 2-D array. We show that a subaperture beamforming (SAB) method tuned to the direction of noise correlation is an effective method to enhance LoSCAN's performance. We evaluated the efficacy of the proposed methods using fundamental and harmonic channel data acquired from the liver of two healthy volunteers using a 64×16 custom 2-D array. Compared to azimuthal LoSCAN, the proposed approach increased the contrast by up to 5.5 dB and the generalized contrast-to-noise ratio (gCNR) by up to 0.07. We also present analytic models to understand the impact of partially correlated reverberation clutter on LoSCAN images and explain the proposed methods' mechanism of image quality improvement.

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging-Part II: Phantom and In Vivo Experiments.

Conventional color flow processing is associated with a high degree of operator dependence, often requiring the careful tuning of clutter filters and priority encoding to optimize the display and accuracy of color flow images. In a companion paper, we introduced a novel framework to adapt color flow processing based on local measurements of backscatter spatial coherence. Through simulation studies, the adaptive selection of clutter filters using coherence image quality characterization was demonstrated as a means to dynamically suppress weakly-coherent clutter while preserving coherent flow signal in order to reduce velocity estimation bias. In this study, we extend previous work to evaluate the application of coherence-adaptive clutter filtering (CACF) on experimental data acquired from both phantom and in vivo liver and fetal vessels. In phantom experiments with clutter-generating tissue, CACF was shown to increase the dynamic range of velocity estimates and decrease bias and artifact from flash and thermal noise relative to conventional color flow processing. Under in vivo conditions, such properties allowed for the direct visualization of vessels that would have otherwise required fine-tuning of filter cutoff and priority thresholds with conventional processing. These advantages are presented alongside various failure modes identified in CACF as well as discussions of solutions to mitigate such limitations.

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging-Part I: Simulation Studies.

The appropriate selection of a clutter filter is critical for ensuring the accuracy of velocity estimates in ultrasound color flow imaging. Given the complex spatio-temporal dynamics of flow signal and clutter, however, the manual selection of filters can be a significant challenge, increasing the risk for bias and variance introduced by the removal of flow signal and/or poor clutter suppression. We propose a novel framework to adaptively select clutter filter settings based on color flow image quality feedback derived from the spatial coherence of ultrasonic backscatter. This framework seeks to relax assumptions of clutter magnitude and velocity that are traditionally required in existing adaptive filtering methods to generalize clutter filtering to a wider range of clinically-relevant color flow imaging conditions. In this study, the relationship between color flow velocity estimation error and the spatial coherence of clutter filtered channel signals was investigated in Field II simulations for a wide range of flow and clutter conditions. This relationship was leveraged in a basic implementation of coherence-adaptive clutter filtering (CACF) designed to dynamically adapt clutter filters at each imaging pixel and frame based on local measurements of spatial coherence. In simulation studies with known scatterer and clutter motion, CACF was demonstrated to reduce velocity estimation bias while maintaining variance on par with conventional filtering.

Frequency-Dependent Spatial Coherence in Conventional and Chirp Transmissions.

The development of adaptive imaging techniques is contingent on the accurate and repeatable characterization of ultrasonic image quality. Adaptive transmit frequency selection, filtering, and frequency compounding all offer the ability to improve target conspicuity by balancing the effects of imaging resolution, the signal-to-clutter ratio, and speckle texture, but these strategies rely on the ability to capture image quality at each desired frequency. We investigate the use of broadband linear frequency-modulated transmissions, also known as chirps, to expedite the interrogation of frequency-dependent tissue spatial coherence for real-time implementations of frequency-based adaptive imaging strategies. Chirp-collected measurements of coherence are compared to those acquired by individually transmitted conventional pulses over a range of fundamental and harmonic frequencies, in order to evaluate the ability of chirps to recreate conventionally acquired coherence. Simulation and measurements in a uniform phantom free of acoustic clutter indicate that chirps replicate not only the mean coherence in a region-of-interest but also the distribution of coherence values over frequency. Results from acquisitions in porcine abdominal and human liver models show that prediction accuracy improves with chirp length. Chirps are also able to predict frequency-dependent decreases in coherence in both porcine abdominal and human liver models for fundamental and pulse inversion harmonic imaging. This work indicates that the use of chirps is a viable strategy to improve the efficiency of variable frequency coherence mapping, thus presenting an avenue for real-time implementations for frequency-based adaptive strategies.

Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets.

Multi-covariate Imaging of Sub-resolution Targets (MIST) is an estimation-based method of imaging the statistics of diffuse scattering targets. MIST estimates the contributions of a set of covariance models to the echo data covariance matrix. Models are defined based on a spatial decomposition of the theoretical transmit intensity distribution into ON-axis and OFF-axis contributions, delineated by a user-specified spatial cutoff. We define this cutoff as the region of interest width (ROI width). In our previous work, we selected the ROI width as the first zero crossing separating the mainlobe from the sidelobe regions. This article explores the effects of varying two key parameters on MIST image quality: 1) ROI width and 2) the degree of spatial averaging of the measured echo data covariance matrix. These results demonstrate a fundamental tradeoff between resolution and speckle texture. We characterize MIST imaging performance across these tunable parameters in a number of simulated, phantom, and in vivo liver applications. We consider performance in noise, fidelity to native contrast, resolution, and speckle texture. MIST is also compared with varying levels of spatial and frequency compounding, demonstrating quantitative improvements in image quality at comparable levels of speckle reduction. In an in vivo example, optimized MIST images demonstrated 20.2% and 13.4% improvements in contrast-to-noise ratio over optimized spatial and frequency compounding images, respectively. These results present a framework for selecting MIST parameters to maximize speckle signal-to-noise ratio without an appreciable loss in resolution.

Incoherent Clutter Suppression Using Lag-One Coherence.

The lag-one coherence (LOC), derived from the correlation between the nearest-neighbor channel signals, provides a reliable measure of clutter which, under certain assumptions, can be directly related to the signal-to-noise ratio of individual channel signals. This offers a direct means to decompose the beamsum output power into contributions from speckle and spatially incoherent noise originating from acoustic clutter and thermal noise. In this study, we applied a novel method called lag-one spatial coherence adaptive normalization (LoSCAN) to locally estimate and compensate for the contribution of spatially incoherent clutter from conventional delay-and-sum (DAS) images. Suppression of incoherent clutter by LoSCAN resulted in improved image quality without introducing many of the artifacts common to other adaptive imaging methods. In simulations with known targets and added channel noise, LoSCAN was shown to restore native contrast and increase DAS dynamic range by as much as 10-15 dB. These improvements were accompanied by DAS-like speckle texture along with reduced focal dependence and artifact compared with other adaptive methods. Under in vivo liver and fetal imaging conditions, LoSCAN resulted in increased generalized contrast-to-noise ratio (gCNR) in nearly all matched image pairs ( N = 366 ) with average increases of 0.01, 0.03, and 0.05 in good-, fair-, and poor-quality DAS images, respectively, and overall changes in gCNR from -0.01 to 0.20, contrast-to-noise ratio (CNR) from -0.05 to 0.34, contrast from -9.5 to -0.1 dB, and texture µ/σ from -0.37 to -0.001 relative to DAS.

MimickNet, Mimicking Clinical Image Post- Processing Under Black-Box Constraints.

Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beams into the approximate Dynamic Tissue Contrast Enhanced (DTCE™) post-processed images found on Siemens clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to DAS data. This flexibility allows MimickNet to hypothetically approximate any manufacturer's post-processing without access to the pre-processed data. MimickNet post-processing achieves a 0.940 ± 0.018 structural similarity index measurement (SSIM) compared to clinical-grade post-processing on a 400 cine-loop test set, 0.937 ± 0.025 SSIM on a prospectively acquired dataset, and 0.928 ± 0.003 SSIM on an out-of-distribution cardiac cine-loop after gain adjustment. To our knowledge, this is the first work to establish deep learning models that closely approximate ultrasound post-processing found in current medical practice. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. Additionally, it can be used as a pretrained model for fine-tuning towards different post-processing techniques. To this end, we have made the MimickNet software, phantom data, and permitted in vivo data open-source at https://github.com/ouwen/MimickNet.

Synthetic Aperture Focusing for Multi-Covariate Imaging of Sub-Resolution Targets.

Coherence-based imaging methods suffer from reduced image quality outside the depth of field for focused ultrasound transmissions. Synthetic aperture methods can extend the depth of field by coherently compounding time-delayed echo data from multiple transmit events. Recently, our group has presented the Multi-covariate Imaging of Sub-resolution Targets (MIST), an estimation-based method to image the statistical properties of diffuse targets. MIST has demonstrated improved image quality over conventional delay-and-sum, but like many coherence-based imaging methods, suffers from limited depth of field artifacts. This article applies synthetic aperture focusing to MIST, which is evaluated using focused, plane-wave, and diverging-wave transmit geometries. Synthetic aperture MIST is evaluated in simulation, phantom, and in vivo applications, demonstrating consistent improvements in contrast-to-noise ratio (CNR) over conventional dynamic receive MIST outside the transmit depth of field, with approximately equivalent results between synthetic transmit geometries. In vivo synthetic aperture MIST images demonstrated 16.8 dB and 16.6% improvements in contrast and CNR, respectively, over dynamic receive MIST images, as well as 17.4 dB and 32.3% improvements over synthetic aperture B-Mode. MIST performance is characterized in the space of plane-wave imaging, where the total plane-wave count is reduced through coarse angular sampling or total angular span. Simulation and experimental results indicate wide applicability of MIST to synthetic aperture imaging methods.

Speckle coherence of piecewise-stationary stochastic targets.

The van Cittert-Zernike (VCZ) theorem describes the propagation of spatial covariance from an incoherent source distribution, such as backscatter from stochastic targets in pulse-echo imaging. These stochastic targets are typically assumed statistically stationary and spatially incoherent with uniform scattering strength. In this work, the VCZ theorem is applied to a piecewise-stationary scattering model. Under this framework, the spatial covariance of the received echo data is demonstrated as the linear superposition of covariances from distinct spatial regions. This theory is analytically derived from fundamental physical principles, and validated through simulation studies demonstrating superposition and scaling. Simulations show that linearity is preserved over various depths and transmit apodizations, and in the presence of noise. These results provide a general framework to decompose spatial covariance into contributions from distinct regions of interest, which may be applied to advanced imaging methods. While the simulation tools used for validation are specific to ultrasound, this analysis is generally applicable to other coherent imaging applications involving stochastic targets. This covariance decomposition provides the physical basis for a recently described imaging method, Multi-covariate Imaging of Sub-resolution Targets.

Multi-covariate Imaging of Sub-resolution Targets.

Conventional B-mode ultrasound imaging assumes that targets consist of collections of point scatterers. Diffraction, however, presents a fundamental limit on a scanner's ability to resolve individual scatterers in most clinical imaging environments. Well-known optics and ultrasound literature has characterized these diffuse scattering targets as spatially incoherent and statistically stationary. In this paper, we apply a piecewise-stationary statistical model to diffuse scattering targets, in which the covariance of backscattered echoes can be described as the linear superposition of constituent components corresponding to echoes from distinct spatial regions in the field. Using this framework, we present Multi-covariate Imaging of Sub-resolution Targets (MIST), a novel estimation-based method to image the statistical properties of diffuse scattering targets, based on a decomposition of aperture domain spatial covariance. The mathematical foundations of the estimator are analytically derived, and MIST is evaluated in phantom, simulation, and in vivo studies, demonstrating consistent improvements in contrast-to-noise ratio and speckle statistics across imaging targets, without an apparent loss in resolution.

Non-invasive Measurement of Dynamic Myocardial Stiffness Using Acoustic Radiation Force Impulse Imaging.

Myocardial stiffness exhibits cyclic variations over the course of the cardiac cycle. These trends are closely tied to the electromechanical and hemodynamic changes in the heart. Characterization of dynamic myocardialstiffness can provide insights into the functional state of the myocardium, as well as allow for differentiation between the underlying physiologic mechanisms that lead to congestive heart failure. Previous work has revealed the potential of acoustic radiation force impulse (ARFI) imaging to capture temporal trends in myocardial stiffness in experimental preparations such as the Langendorff heart, as well as on animals in open-chest and intracardiac settings. This study was aimed at investigating the potential of ARFI to measure dynamic myocardial stiffness in human subjects, in a non-invasive manner through transthoracic imaging windows. ARFI imaging was performed on 12 healthy volunteers to track stiffness changes within the interventricular septum in parasternal long-axis and short-axis views. Myocardial stiffness dynamics over the cardiac cycle was quantified using five indices: stiffness ratio, rates of relaxation and contraction and time constants of relaxation and contraction. The yield of ARFI acquisitions was evaluated based on metrics of signal strength and tracking fidelity such as displacement signal-to-noise ratio, signal-to-clutter level, temporal coherence of speckle and spatial similarity within the region of excitation. These were quantified using the mean ARF-induced displacements over the cardiac cycle, the contrast between the myocardium and the cardiac chambers, the minimum correlation coefficients of radiofrequency signals and the correlation between displacement traces across simultaneously acquired azimuthal beams, respectively. Forty-one percent of ARFI acquisitions were determined to be "successful" using a mean ARF-induced displacement threshold of 1.5 µm. "Successful" acquisitions were found to have higher (i) signal-to-clutter levels, (ii) temporal coherence and (iii) spatial similarity compared with "unsuccessful" acquisitions. Median values of these three metrics, between the two groups, were measured to be 13.42dB versus 5.42dB, 0.988 versus 0.976 and 0.984 versus 0.849, respectively. Signal-to-clutter level, temporal coherence and spatial similarity were also found to correlate with each other. Across the cohort of healthy volunteers, the stiffness ratio measured was 2.74 ± 0.86; the rate of relaxation, 7.82 ± 4.69/s; and the rate of contraction, -7.31±3.79 /s. The time constant of relaxation was 35.90 ± 20.04ms, and that of contraction was 37.24 ± 19.85ms. ARFI-derived indices of myocardial stiffness were found to be similar in both views. These results indicate the feasibility of using ARFI to measure dynamic myocardial stiffness trends in a non-invasive manner and also highlightthe technical challenges of implementing this method in the transthoracic imaging environment.

Short-lag Spatial Coherence Imaging in 1.5-D and 1.75-D Arrays: Elevation Performance and Array Design Considerations.

Short-lag spatial coherence (SLSC) imaging has demonstrated improved performance over conventional B-Mode ultrasound imaging. Previous work has evaluated the performance of SLSC using 2-D matrix arrays in simulation and in vivo studies across various levels of subaperture beamforming, demonstrating improved contrast-to-noise ratio (CNR) and speckle signal-to-noise ratio (SNR) over 1-D arrays. This work explores the application of SLSC imaging in 1.5-D and 1.75-D arrays to quantify the impacts of elevation element count, mirroring, and Fresnel element spacing on SLSC image quality. Through simulation and in vivo studies, increased elevation element count was shown to improve CNR and speckle SNR relative to 1-D SLSC and B-Mode images. Elevation mirroring (1.5-D) was shown to force the inclusion of long lags into the SLSC calculation, introducing additional decorrelation and reducing image quality relative to 1.75-D arrays with individually-connected elements. These results demonstrate the effectiveness of SLSC imaging in 1.5-D and 1.75-D arrays.

Measuring Intraventricular Pressure Using Ultrasound Elastography.

OBJECTIVES: Intraventricular pressure (IVP) is one of the most important measurements for evaluating cardiac function, but this measurement is not currently easily assessable in the clinic. The primary reason for this is the absence of a noninvasive technique for measuring IVP. In this study, we investigate the relationship between IVP and dynamic myocardial stiffness measured by shear wave elasticity imaging (SWEI) and assess the feasibility of measuring IVP using SWEI. METHODS: In 8 isolated working rabbit hearts, IVP was recorded in the left ventricle using a pressure catheter. Simultaneously, myocardial stiffness was recorded by SWEI. Using the peak values for IVP and SWEI measured stiffness, SWEI measurements were calibrated and converted to IVP. RESULTS: A linear relationship with zero intercept was observed between IVP and SWEI, with the average slope of 0.318 kPa/mm Hg, R2 = 0.89. Using one point on the IVP/SWEI curve, SWEI measurements were converted to IVP. Estimated pressure using SWEI and IVP were linearly correlated with the slope of 0.95, R2 = 0.88 (mean end diastolic pressure by pressure catheter = 12.716 mm Hg and by SWEI=14.726 mm Hg), indicating the near equivalence of the 2 measurements. CONCLUSION: We have shown that SWEI measurements are linearly related to IVP; therefore, pressure-based indices could potentially be derived from SWEI ultrasound elastography. The feasibility of using SWEI to estimate IVP with a single point calibration was also shown in this study.

Lag-One Coherence as a Metric for Ultrasonic Image Quality.

Reliable assessment of image quality is an important but challenging task in complex imaging environments such as those encountered in vivo. To address this challenge, we propose a novel imaging metric, known as the lag-one coherence (LOC), which leverages the spatial coherence between nearest-neighbor array elements to provide a local measure of thermal and acoustic noise. In this paper, we derive the theory that relates LOC and the conventional image quality metrics of contrast and contrast-to-noise ratio (CNR) to channel noise. Simulation and phantom studies are performed to validate this theory and compare the variability of LOC to that of conventional metrics. We further evaluate the performance of LOC using matched measurements of contrast, CNR, and temporal correlation from in vivo liver images formed with varying mechanical index (MI) to assess the feasibility of adaptive acoustic output selection using LOC feedback. Simulation and phantom results reveal a lower variability in LOC relative to contrast and CNR over a wide range of clinically relevant noise levels. This improved stability is supported by in vivo measurements of LOC which show an increased monotonicity with changes in MI compared to matched measurements of contrast and CNR (88.6% and 85.7% of acquisitions, respectively). The sensitivity of LOC to stationary acoustic noise is evidenced by positive correlations between LOC and contrast ( ) and LOC and CNR ( ) at high acoustic output levels in the absence of thermal noise. Results indicate that LOC provides repeatable characterization of patient-specific trends in image quality, demonstrating feasibility in the selection of acoustic output using LOC and its application for in vivo image quality assessment.