LAUSR

Effective tissue clutter filtering and noise removing are essential for ultrafast Doppler imaging. Singular vector decomposition (SVD) based spatiotemporal method has been applied as a classical method to remove the clutter and strong motion artifacts. However, performance of the SVD-based methods often depends on a proper eigenvector thresholding, i.e., the separation of signal subspaces of small-value blood flow, large-value static tissue and noise. In the study, a Cauchy-norm based robust principal component analysis (Cauchy-RPCA) method is developed via Cauchy-norm based sparsity penalization, which enhances the blood flow extraction of small-vessels. A randomized spatial downsampling strategy and alternating direction method of multipliers (ADMM) are further involved to accelerate the computation. A face to face comparison is carried out among the classical SVD, traditional RPCA, BD-RPCA, and the proposed Cauchy-RPCA methods. Ultrafast ultrasound imaging dataset recorded from rat brain is used to investigate the performance of the proposed Cauchy-RPCA method in terms of clutter filtering, power Doppler, color Doppler and functional ultrasound imaging. The computational efficiency is finally discussed.