1K-1 A New Eigen-Based Clutter Filter Using the Hankel-SVD Approach

In color flow data processing, the eigen-regression filter has shown potential in suppressing slow-time clutter while preserving blood echoes because of its adaptability to the Doppler signal contents. However, this filter is inherently based on the use of multiple slow-time snapshots that are statistically stationary. In this article, we present a new eigen-based clutter filter called the Hankel-SVD filter that does not involve the use of multiple slow-time snapshots in its formulation. The new filter, which is derived using the notion of principal Hankel component analysis, works by exploiting the eigen-space properties of a matrix form known as the Hankel matrix. To assess its efficacy, the Hankel-SVD filter was applied to synthesized slow-time data with arterial flow parameters and low-velocity flow parameters as well as in vivo color flow imaging data obtained from the carotid arteries of a healthy youth. It was found that the new filter generally has better flow detection performance than the clutter-downmixing filter and a fixed-rank multi-snapshot-based eigen-filter