  Effective clutter filtering is crucial in suppressing tissue clutter and
extracting blood flow signal in Doppler ultrasound. Recent advances in
eigen-based clutter filtering techniques have enabled ultrasound imaging of
microvasculature without the need for contrast agents. However, simultaneously
achieving fully adaptive, highly sensitive and real-time implementation of such
eigen-based filtering techniques in clinical scanning scenarios for broad
translation remains challenging. To address this, here we propose a fast
spatiotemporal clutter filtering technique based on eigenvalue decomposition
(EVD) and a novel localized data processing framework for robust and
high-definition ultrasound imaging of blood flow. Unlike the existing local
clutter filter that hard splits the ultrasound data into small blocks, our
approach applies a series of 2D spatial Gaussian windows to the original data
to generate local data subsets. This approach improves performance of flow
detection while effectively avoiding undesired grid artifacts with dramatically
reduced number of subsets required in local EVD filtering to shorten
computation time. By leveraging the computational power of Graphics Processing
Units (GPUs), we demonstrate the real-time implementation capability of the
proposed approach. We also introduce and systematically evaluate several
adaptive and automatic eigenvalue thresholding methods tailored for EVD-based
filtering to facilitate optimization of blood flow imaging for either global or
localized processing. The feasibility of the proposed clutter filtering
technique is validated by experimental results from phantom and different in
vivo studies, revealing robust clinical application potential. A tradeoff
between improved performance and computational cost associated with the packet
size and subset number in local processing is also investigated.
