Publications by authors named "Trahey G"

Sound speed estimation can potentially correct the focusing errors in medical ultrasound. Maximizing the echo spatial coherence as a function of beamforming sound speed is a known technique to estimate the average sound speed. However, beamformation with changing sound speed causes a spatial shift of the echo signals resulting in noise and registration errors in the average sound speed estimates.

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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 as low as reasonably achievable (ALARA) 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.

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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).

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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.

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Article Synopsis
  • Deep abdominal imaging faces challenges with resolution, which can be improved by increasing the aperture size; however, phase distortion and clutter can reduce the effectiveness of larger arrays.
  • The study used an 8.8-cm linear array transducer to examine how different aperture sizes affect imaging through the abdominal wall, employing various techniques including synthetic aperture data synthesis.
  • Results indicated improved point resolution with larger apertures, although contrast resolution sometimes degraded; nonetheless, larger apertures enhanced the ability to detect vascular targets and showed overall benefits in tissue-harmonic imaging.
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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.

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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.

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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.

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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.

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Traditional pulse-echo ultrasound imaging heavily relies on the discernment of signals based on their relative magnitudes but is limited in its ability to mitigate sources of image degradation, the most prevalent of which is acoustic clutter. Advances in computing power and data storage have made it possible for echo data to be alternatively analyzed through the lens of spatial coherence, a measure of the similarity of these signals received across an array. Spatial coherence is not currently explicitly calculated on diagnostic ultrasound scanners but a large number of studies indicate that it can be employed to describe image quality, to adaptively select system parameters and to improve imaging and target detection.

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Ultrasound is an essential tool for diagnosing and monitoring diseases, but it can be limited by poor image quality. Lag-one coherence (LOC) is an image quality metric that can be related to signal-to-noise ratio and contrast-to-noise ratio. In this study, we examine matched LOC and B-mode images of the liver to discern patterns of low image quality, as indicated by lower LOC values, occurring beneath the abdominal wall, near out-of-plane vessels and adjacent to hyperechoic targets such the liver capsule.

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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.

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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.

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Objectives: Ultrasound users are advised to observe the ALARA (as low as reasonably achievable) principle, but studies have shown that most do not monitor acoustic output metrics. We developed an adaptive ultrasound method that could suggest acoustic output levels based on real-time image quality feedback using lag-one coherence (LOC).

Methods: Lag-one coherence as a function of the mechanical index (MI) was assessed in 35 healthy volunteers in their second trimester of pregnancy.

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Cardiac imaging depends on clear visualization of several different structural and functional components to determine left ventricular and overall cardiac health. Ultrasound imaging is confounded by the characteristic speckle texture resulting from subwavelength scatterers in tissues, which is similar to a multiplicative noise on underlying tissue structure. Reduction of this texture can be achieved through physical means, such as spatial or frequency compounding, or through adaptive image processing.

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The magnitudes by which aberration and incoherent noise sources, such as diffuse reverberation and thermal noise, contribute to degradations in image quality in medical ultrasound are not well understood. Theory predicting degradations in spatial coherence and contrast in response to combinations of incoherent noise and aberration levels is presented, and the theoretical values are compared to those from simulation across a range of magnitudes. A method to separate the contributions of incoherent noise and aberration in the spatial coherence domain is also presented and applied to predictions for losses in contrast.

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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.

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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.

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Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast).

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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.

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Abdominal imaging suffers from a particularly difficult acoustic environment-targets are located deep and overlying tissue layers with varying properties generate acoustic clutter. Increasing array size can overcome the penetration and lateral resolution problems in ideal conditions, but how the impact of clutter scales with increasing array extent is unknown and may limit the benefits in vivo. Previous ex vivo experimental work showed the promise of large arrays but was technically limited to a length of 6.

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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.

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Kidney stone disease is a major health problem worldwide. Shockwave lithotripsy (SWL), which uses high-energy shockwave pulses to break up kidney stones, is extensively used in clinic. However, despite its noninvasiveness, SWL can produce cavitation in vivo.

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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.

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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.

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