Vector flow imaging (VFI) estimates blood flow velocities in both azimuth and axial dimensions. VFI has promising applications in the characterization of complex flow patterns, including cardiac flow and abdominal flow imaging. Conventional VFI relies on the use of multiple angles in transmit or receive, or speckle tracking. They are computationally intensive and estimate quality may be sacrificed to improve computational speed. In this work, we report a vector flow estimation technique using a deep neural network. The network extracts feature from high-pass filtered beamsummed RF data of two consecutive Doppler packets. For each packet, the RF data have azimuth and axial dimensions. It then performs estimation of vector flow in the feature space, and maps the estimate back to the spatial domain. The total computation time is 0.11 s for a pair of Doppler frames of 1024 × 128 samples. The performance of the method is characterized using Field II simulation studies, flow phantom studies, and an in vivo liver study with a Verasonics Vantage 256 scanner. The simulation and flow phantom studies show good agreement between the estimates and the ground truth. The in vivo studies demonstrate that the method is capable of characterizing complex flow patterns in human liver vessels.
Power Doppler imaging is traditionally the flow imaging technique utilized clinically in the task of flow detection. Due to the relatively low pulse repetition frequencies used, power Doppler ensemble sizes are small to allow real-time imaging. The small ensemble size, however, makes power Doppler imaging subject to the so-called flash artifact due to limited stationary clutter filtering capabilities. In addition, with the increasing population of overweight and obese individuals, power Doppler imaging is subject to higher amounts of thermal noise and reverberation artifact that pass through the stationary clutter filters. We present coherence beamforming techniques, applied to in vivo human imaging, to demonstrate their ability to reduce flow artifacts and noise in power Doppler imaging. Using coherence beamforming techniques in Coherent Flow Power Doppler (CFPD), we show that artifacts due to reverberation and thermal noise are reduced, corresponding to an increase of 7.5 dB in signal-to-noise ratio, in liver imaging of overweight and obese individuals. Similarly, we demonstrate coherence beamforming techniques in CFPD to reduce flash artifacts in videos of the neonatal brain.
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