Adaptive Clutter Filtering for Ultrafast Doppler Imaging of Blood Flow Using Fast Multivariate Empirical Mode Decomposition

The rapid development of ultrafast ultrasound imaging based on the unfocused transmission of plane-wave has led to the widespread attention of clutter filtering technology, since the discrimination between tissue and blood motion is critical for non-contrast ultrafast Doppler imaging of blood flow. The increasingly used clutter filtering method, i.e., the singular value decomposition (SVD) is essentially a black-box technique, and the direct rejection of any singular vector corresponding to the first singular values will result in the potential loss of blood flow or residue of tissue activities. To cater for a better filtering performance, an adaptive clutter rejection method based on the fast multivariate empirical mode decomposition (FMEMD) is proposed, and compared to the high-pass filter (HPF) and SVD in the task of performing blood flow imaging and velocity profile estimation on signals collected from carotid arteries of 10 healthy human subjects. The results demonstrate the superiority of the proposed method over the state-of-the-art techniques, especially for discriminating between blood flow and tissue signals near the vessel walls.