We demonstrate a novel imaging technique, named short-lag spatial coherence (SLSC) imaging, which uses short distance (or lag) values of the coherence function of backscattered ultrasound to create images. Simulations using Field II are used to demonstrate the detection of lesions of varying sizes and contrasts with and without acoustical clutter in the backscattered data. B-mode and SLSC imaging are shown to be nearly equivalent in lesion detection, based on the contrast-to-noise ratio (CNR) of the lesion, in noise-free conditions. The CNR of the SLSC image, however, can be adjusted to achieve an optimal value at the expense of image smoothness and resolution. In the presence of acoustic clutter, SLSC imaging yields significantly higher CNR than B-mode imaging and maintains higher image quality than B-mode with increasing noise. Compression of SLSC images is shown to be required under high-noise conditions, but is unnecessary under no- and low-noise conditions. SLSC imaging is applied to in vivo imaging of the carotid sheath, and demonstrates significant gains in CNR as well as visualization of arterioles in the carotid sheath. SLSC imaging has a potential application to clutter rejection in ultrasonic imaging.

With traditional beamforming methods, ultrasound B-mode images contain speckle noise caused by the random interference of subresolution scatterers. In this paper, we present a framework for using neural networks to beamform ultrasound channel signals into speckle-reduced B-mode images. We introduce log-domain normalization-independent loss functions that are appropriate for ultrasound imaging. A fully convolutional neural network was trained with simulated channel signals that were co-registered spatially to ground truth maps of echogenicity. Networks were designed to accept 16 beamformed subaperture radiofrequency signals. Training performance was compared as a function of training objective, network depth, and network width. The networks were then evaluated on simulation, phantom, and in vivo data and compared against existing speckle reduction techniques. The most effective configuration was found to be the deepest (16 layer) and widest (32 filter) networks, trained to minimize a normalization-independent mixture of the ℓ 1 and multi-scale structural similarity losses. The neural network significantly outperformed delay-and-sum and receive-only spatial compounding in speckle reduction while preserving resolution and exhibited improved detail preservation over a non-local means methods. This work demonstrates that ultrasound B-mode image reconstruction using machine-learned neural networks is feasible and establishes that networks trained solely in silico can be generalized to real-world imaging in vivo to produce images with significantly reduced speckle.

A model and method to accurately estimate the local speed of sound in tissue from pulse-echo ultrasound data is presented. The model relates the local speeds of sound along a wave propagation path to the average speed of sound over the path, and allows one to avoid bias in the sound-speed estimates that can result from overlying layers of subcutaneous fat and muscle tissue. Herein, the average speed of sound using the approach by Anderson and Trahey is measured, and then the authors solve the proposed model for the local sound-speed via gradient descent. The sound-speed estimator was tested in a series of simulation and ex vivo phantom experiments using two-layer media as a simple model of abdominal tissue. The bias of the local sound-speed estimates from the bottom layers is less than 6.2 m/s, while the bias of the matched Anderson's estimates is as high as 66 m/s. The local speed-of-sound estimates have higher standard deviation than the Anderson's estimates. When the mean local estimate is computed over a 5-by-5 mm region of interest, its standard deviation is reduced to less than 7 m/s.

Power Doppler (PD) imaging is a widely used technique for flow detection. Despite the wide use of Doppler ultrasound, limitations exist in the ability of Doppler ultrasound to assess slow flow in small-diameter vasculature, such as the maternal spiral arteries and fetal villous arteries of the placenta and focal liver lesions. The sensitivity of PD in small vessel detection is limited by the low signal produced by slow flow and the noise associated with small vessels. The noise sources include electronic noise, stationary or slowly moving tissue clutter, reverberation clutter, and off-axis scattering from tissue, among others. In order to provide more sensitive detection of slow flow in small diameter vessels, a coherent flow imaging technique, termed coherent flow power Doppler (CFPD), is characterized and evaluated with simulation, flow-phantom experiment studies, and an in vivo animal small vessel detection study. CFPD imaging was introduced as a technique to detect slow blood flow. It has been demonstrated to detect slow flow below the detection threshold of conventional power Doppler (PD) imaging using identical pulse sequences and filter parameters. In this study, we compare CFPD to PD in the detection of blood flow in small-diameter vessels. The results from the study suggest that CFPD is able to provide 7.5–12.5 dB increase in the signal-to-noise ratio (SNR) over power Doppler images for the same physiological conditions, and is less susceptible to reverberation clutter and thermal noise. Due to the increase in SNR, CFPD is able to detect small vessels in high channel noise cases, for which PD was unable to generate enough contrast to observe the vessel.

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