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(Early Access) Acoustic clutter is a primary source of image degradation in ultrasound imaging. In the context of flow imaging, tissue and acoustic clutter signals are often much larger in magnitude than the blood signal, which limits the sensitivity of conventional power Doppler in SNR-limited environments. This has motivated the development of coherence-based beamformers, including Coherent Flow Power Doppler (CFPD), which have demonstrated efficacy in mitigating sources of diffuse clutter. However, CFPD uses a measure of normalized coherence, which incurs a non-linear relationship between image intensity and the magnitude of the blood echo. As a result, CFPD is not a robust approach to study gradation of blood signal energy, which depicts the fractional moving blood volume. We propose the application of mutual intensity, rather than normalized coherence, to retain the clutter suppression capability inherent in coherence beamforming, while preserving the underlying signal energy. Feasibility of this approach was shown via Field II simulations, phantoms, and in vivo human liver data. In addition, we derive an adaptive statistical threshold for the suppression of residual noise signals. Overall, this beamformer design shows promise as an alternative technique to depict flow volume gradation in cluttered imaging environments. 

Eigen-based clutter filtering of Doppler data has demonstrated greater clutter rejection performance than traditional filtering in a number of studies. However, practical translation of these eigen-based techniques to channel domain filtering applications is limited by their high computational burden. To enable efficient eigen-based filtering of channel data, we propose a domain-adaptive filtering framework. This technique involves using a basis set generated from RF data to filter delayed channel data. Preliminary findings suggest that this technique retains superior clutter rejection performance in comparison to conventional techniques. 

Ultrasonic flow imaging remains susceptible to cluttered imaging environments, which often results in degraded image quality. Coherent Flow Power Doppler (CFPD) is a beamforming technique that has demonstrated efficacy in mitigating the presence of diffuse clutter in flow images. CFPD depicts the normalized aperture domain coherence of the backscattered echo, which is described by the van Cittert-Zernike theorem. However, the use of a normalized coherence metric uncouples the image intensity from the underlying blood signal energy. As a result, CFPD is not a robust approach to study gradation in blood signal energy, which depicts the fractional moving blood volume. We have developed a modified beamforming scheme, termed power-preserving Coherent Flow Power Doppler (ppCFPD), which depicts a measure of mutual intensity, rather than normalized coherence. This approach retains the clutter suppression capability of CFPD, while preserving sensitivity toward the underlying signal energy, similar to conventional power Doppler. Efficacy of this approach was shown via Field II simulations, and in vivo feasibility was demonstrated in a human liver. Overall, this adapted approach shows promise as an alternative technique to depict flow gradation in cluttered imaging environments.