A locally adaptive Casorati-SVD clutter filtering to improve ultrafast color Doppler imaging

For ultrafast color Doppler, the singular value decomposition (SVD) algorithm based on the spatial difference of the Doppler signals is more suitable to suppress clutter than the high-pass filter, but is limited with the determination of the filtering threshold. In this paper, a locally adaptive Casorati-SVD clutter filtering method is proposed to improve the color flow imaging with ultrafast ultrasound. The time sequence of radio frequency (RF) signals is divided into several local regions in the two-dimensional scanning plane, and reconstructed separately to generate Casorati matrices. The singular value decomposition is performed on these matrices to obtain the singular value energy series curve and its second derivative. Clutter components, whose singular value is higher than the inflection point of the second derivative curve are filtered out. In in-vivo experiments based on 10 healthy human carotid arteries, color flow imaging results obtained with the proposed method exhibit more complete vessel lumen and more accurate low-velocities than those obtained with the conventional SVD with global threshold. Furthermore, the goodness-fit R2 of the blood flow velocity profile is increased by 13.04% on average. In conclusion, the proposed method is helpful for ultrafast ultrasound color blood flow imaging in clinical diagnosis.