Reliable blood flow measurements in the neonatal brain are difficult to obtain with conventional Power Doppler (PD) due to small vessel size, slow flow, and strong reverberation from the cranium. Under such imaging conditions, it is important to use long ensemble lengths and to reduce the acoustic noise in order to separate the slow-flow signal from the stationary-tissue clutter. We have recently developed the short-lag angular coherence (SLAC) beamforming method to reduce noise in the Doppler data, and used it to track blood-flow changes in the brains of neonates. SLAC suppresses the incoherent portion of the beam-summed signals and utilizes Fourier beamforming for fast processing of large Doppler ensembles. To remove stationary tissue signal from the data, we have also utilized spatiotemporal filtering prior to the SLAC processing step. The matching frames of SLAC-based PD and conventional PD were reconstructed from the same Doppler data captured on the neonatal brain vasculature over 4 cm depth. To achieve a fair comparison, the Doppler signal of each modality was normalized by its respective noise profile measured as a function of depth from a stationary speckle phantom. The SLAC images showed better delineation of small vessels, and the vessel SNR was measured to be up to 2 dB higher in SLAC images than in matching PD images. To demonstrate the quantitative aspect of SLAC-based PD, we have also created matched conventional PD and SLAC-based PD videos from the ten-second Doppler scans of neonatal brains. For the vasculature of interest, integrated pixel intensity was computed as a function of time. SLAC-based PD was able to capture changes in the cortical flow, and it closely followed the corresponding conventional PD signal for the duration of the acquisition. No external stimuli were applied during the scans. Normalized cross-correlation between the two signals was 0.991.
When using ultrasound to image heterogeneous media, echoes from multiple and off-axis scattering can overwrite the recorded ballistic wavefronts of interest. This reduces the coherence of signals across the aperture and causes clutter in the final image. Therefore, separating those unwanted events from the signal of interest is necessary to improve the visibility of structures in a B-mode image, and also to enable other processing methods that require coherent channel signals, such as various phase-aberration-correction techniques and sound-speed estimators. We used prediction-error filters (PEFs) to model the signal and the assumed additive noise in the data acquired through a 10 mm thick layer of beef tissue placed above a speckle region of a phantom. The PEF coefficients used to model the signal were first computed from the phantom data collected without tissue and subsequently employed to deconvolve the tissue data and find the PEF associated with the noise. These two filters were then used in a joint-inversion framework to separate the signal and noise components recorded within the original tissue data. In order to be able to apply our method in scenarios where direct measurements of the signal proxy are not available, we also evaluated the signal-PEF coefficients from the theoretical model of the signal from diffuse targets as provided by the van-Cittert Zernike (VCZ) theorem. To evaluate the quality of the separation of signal from the noise, we compared the original channel data acquired through the tissue with its estimated ballistic-wave component, as well as their corresponding spectra. We also compared performance of the proposed technique to F-X filter, which is a popular linear-predictionbased filter used to suppress noise in channel data. After the removal of acoustic noise from the channel data, coherence across the aperture increases. The average nearest-neighbor cross-correlation computed on the original data is 0.47, while the nearest-neighbor cross-correlation of the estimated ballistic-wave component is 0.81 or 0.97, depending whether the experimental or theoretical signal-PEFs are used in the estimation process.
When imaging with ultrasound through the chest wall, it is not uncommon for parts of the array to get blocked by ribs, which can limit the acoustic window and significantly impede visualization of the structures of interest. With the development of large-aperture, high-element-count, 2-D arrays and their potential use in transthoracic imaging, detecting and compensating for the blocked elements is becoming increasingly important. <p> </p>We synthesized large coherent 2-D apertures and used them to image a point target through excised samples of canine chest wall. Blocked elements are detected based on low amplitude of their signals. As a part of compensation, blocked elements are turned off on transmit (Tx) and receive (Rx), and point-target images are created using: coherent summation of the remaining channels, compounding of intercostal apertures, and adaptive weighting of the available Tx/Rx channel-pairs to recover the desired k-space response. The adaptive compensation method also includes a phase aberration correction to ensure that the non-blocked Tx/Rx channel pairs are summed coherently. <p> </p>To evaluate the methods, we compare the point-spread functions (PSFs) and near-field clutter levels for the transcostal and control acquisitions. Specifically, applying k-space compensation to the sparse aperture data created from the control acquisition reduces sidelobes from -6.6 dB to -12 dB. When applied to the transcostal data in combination with phase-aberration correction, the same method reduces sidelobes only by 3 dB, likely due to significant tissue induced acoustic noise. For the transcostal acquisition, turning off blocked elements and applying uniform weighting results in maximum clutter reduction of 5 dB on average, while the PSF stays intact. Compounding reduces clutter by about 3 dB while the k-space compensation increases clutter magnitude to the non-compensated levels.
Ultrasound imaging of deep targets is limited by the resolution of current ultrasound systems based on the available aperture size. We propose a system to synthesize an extended effective aperture in order to improve resolution and target detectability at depth using a precisely-tracked transducer swept across the region of interest. A Field II simulation was performed to demonstrate the swept aperture approach in both the spatial and frequency domains. The adaptively beam-formed system was tested experimentally using a volumetric transducer and an ex vivo canine abdominal layer to evaluate the impact of clutter-generating tissue on the resulting point spread function. Resolution was improved by 73% using a 30.8 degree sweep despite the presence of varying aberration across the array with an amplitude on the order of 100 ns. Slight variations were observed in the magnitude and position of side lobes compared to the control case, but overall image quality was not significantly degraded as compared by a simulation based on the experimental point spread function. We conclude that the swept aperture imaging system may be a valuable tool for synthesizing large effective apertures using conventional ultrasound hardware.
The ability of an ultrasound system to differentiate signals in the presence of clutter is of key
clinical importance. There are several sources of clutter but assessing their relative importance
and developing methods of reducing them remain areas of active research. We have developed
a novel method called short-lag-spatial-coherence (SLSC) imaging that allows formation of high
quality ultrasound images in the presence of clutter. The method is based on the van-Cittert-
Zernike theorem. Specifically, the images are formed by utilizing the spatial coherence of the
pressure field at the surface of the transducer. We compare matched SLSC and B-mode images
beamformed from simulated data and data acquired on human liver in vivo. SLSC images have
higher contrast and CNR then their B-mode counterparts for all acoustic-noise conditions (low,
medium, and high noise). Nevertheless, SLSC brings highest improvement of target detectability
in the medium noise environment. When the received signal is saturated with noise, both Bmode
and SLSC produce low-quality images.