Adaptive ultrasound clutter rejection through spatial eigenvector filtering
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Off-axis clutter is a significant cause of image degradation in ultrasound. Adaptive weighting methods based on signal coherence, and adaptive beamforming approaches based on signal direction of arrival, have been proposed to address this problem. Clutter removal is also an important component of pre-processing prior to flow estimation, and adaptive clutter filters based on singular value decomposition (SVD) to separate clutter from signal have been successfully developed in this context. In this study, we implement SVD clutter suppression with similar principles as those used in flow imaging, and apply it for beamforming: in flow, the goal is to remove low temporal frequency components (clutter moves slowly), while in beamforming, the goal is to remove high spatial frequency components (clutter comes from off-axis). In both, decomposition of the data matrix into singular vectors is expected to maximize separation between real signal and clutter.