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Clutter is a noise artifact in ultrasound images that appears as diffuse echoes overlying signals of interest. It is most easily observed in anechoic or hypoechoic regions, such as in cysts, blood vessels, amniotic fluid, and urine-filled bladders. Clutter often obscures targets of interest and complicates anatomical measurements. An analytical expression that characterizes the extent to which clutter degrades lesion contrast was derived and compared to the measured contrast loss due to clutter in a bladder phantom. Simulation and phantom studies were performed to determine ideal and achievable signal-to-clutter ratios. In vivo clutter magnitudes were quantified in simultaneously-acquired fundamental and harmonic bladder images from five volunteers. Clutter magnitudes ranged from −30 dB to 0 dB, relative to the mean signal of the bladder wall. For this range of clutter magnitudes, the analytical expression predicts a contrast loss of 0–45 dB for lesions with clutter-free contrasts of 6–48 dB. A pixel-wise comparison of simultaneously-acquired fundamental and harmonic bladder images from each volunteer revealed an over all signal reduction in harmonic images, with aver age reductions ranging from 11–18 dB in the bladder interior and 9–11 dB in the tissue surrounding the bladder. Harmonic imaging did not reduce clutter in all volunteers.

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In ultrasound images, clutter is a noise artifact most easily observed in anechoic or hypoechoic regions. It appears as diffuse echoes overlying anatomical structures of diagnostic importance, obscuring tissue borders and reducing image contrast. A novel clutter reduction method for abdominal images is proposed, wherein the abdominal wall is displaced during successive-frame image acquisitions. A region of clutter distal to the abdominal wall was observed to move with the abdominal wall, and finite impulse response (FIR) and blind source separation (BSS) motion filters were implemented to reduce this clutter. The proposed clutter reduction method was tested in simulated and phantom data and applied to fundamental and harmonic in vivo bladder and liver images from 2 volunteers. Results show clutter reductions ranging from 0 to 18 dB in FIR-filtered images and 9 to 27 dB in BSS-filtered images. The contrast-to-noise ratio was improved by 21 to 68% and 44 to 108% in FIR-and BSS-filtered images, respectively. Improvements in contrast ranged from 4 to 12 dB. The method shows promise for reducing clutter in other abdominal images.

A method for estimating the 2-D vector velocity of blood using a phased-array transducer is presented. The approach is based on the transverse oscillation (TO) method. The purposes of this work are to expand the TO method to a phased-array geometry and to broaden the potential clinical applicability of the method. A phased-array transducer has a smaller footprint and a larger field of view than a linear array, and is therefore more suited for, e.g., cardiac imaging. The method relies on suitable TO fields, and a beamforming strategy employing diverging TO beams is proposed. The implementation of the TO method using a phased-array transducer for vector velocity estimation is evaluated through simulation and flow-rig measurements are acquired using an experimental scanner. The vast number of calculations needed to perform flow simulations makes the optimization of the TO fields a cumbersome process. Therefore, three performance metrics are proposed. They are calculated based on the complex TO spectrum of the combined TO fields. It is hypothesized that the performance metrics are related to the performance of the velocity estimates. The simulations show that the squared correlation values range from 0.79 to 0.92, indicating a correlation between the performance metrics of the TO spectrum and the velocity estimates. Because these performance metrics are much more readily computed, the TO fields can be optimized faster for improved velocity estimation of both simulations and measurements. For simulations of a parabolic flow at a depth of 10 cm, a relative (to the peak velocity) bias and standard deviation of 4% and 8%, respectively, are obtained. Overall, the simulations show that the TO method implemented on a phased-array transducer is robust with relative standard deviations around 10% in most cases. The flow-rig measurements show similar results. At a depth of 9.5 cm using 32 emissions per estimate, the relative standard deviation is 9% and the relative bias is -9%. At the center of the vessel, the velocity magnitude is estimated to be 0.25 ± 0.023 m/s, compared with an expected peak velocity magnitude of 0.25 m/s, and the beam-to-flow angle is calculated to be 89.3° ± 0.77°, compared with an expected angle value between 89° and 90°. For steering angles up to ±20° degrees, the relative standard deviation is less than 20%. The results also show that a 64-element transducer implementation is feasible, but with a poorer performance compared with a 128-element transducer. The simulation and experimental results demonstrate that the TO method is suitable for use in conjunction with a phased-array transducer, and that 2-D vector velocity estimation is possible down to a depth of 15 cm.

Abstract-A method for 3-D velocity vector estimation using transverse oscillations is presented. The method employs a 2-D transducer and decouples the velocity estimation into three orthogonal components, which are estimated simultaneously and from the same data. The validity of the method is investigated by conducting simulations emulating a 32 × 32 matrix transducer. The results are evaluated using two performance metrics related to precision and accuracy. The study includes several parameters including 49 flow directions, the SNR, steering angle, and apodization types. The 49 flow directions cover the positive octant of the unit sphere. In terms of accuracy, the median bias is −2%. The precision of v x and v y depends on the flow angle β and ranges from 5% to 31% relative to the peak velocity magnitude of 1 m/s. For comparison, the range is 0.4 to 2% for v z . The parameter study also reveals, that the velocity estimation breaks down with an SNR between −6 and −3 dB. In terms of computational load, the estimation of the three velocity components requires 0.75 billion floating point operations per second (0.75 Gflops) for a realistic setup. This is well within the capability of modern scanners.