A single-ensemble-based hybrid approach to clutter rejection combining bilinear Hankel with regression

Clutter regarded as ultrasound Doppler echoes of soft tissue interferes with the primary objective of color flow imaging (CFI): measurement and display of blood flow. Multi-ensemble samples based clutter filters degrade resolution or frame rate of CFI. The prevalent single-ensemble clutter rejection filter is based on a single rejection criterion and fails to achieve a high accuracy for estimating both the low- and high-velocity blood flow components.

The Bilinear Hankel-SVD achieved more exact signal decomposition than the conventional Hankel-SVD. Furthermore, the correlation between two arbitrary eigen-components obtained by the B-Hankel-SVD was demonstrated. In the hybrid approach, the input ultrasound Doppler signal first passes through a low-order regression filter, and then the output is properly decomposed into a collection of eigen-components under the framework of B-Hankel-SVD. The blood flow components are finally extracted based on a frequency threshold.

In a series of simulations, the proposed B-Hankel-SVD filter reduced the estimation bias of the blood flow over the conventional Hankel-SVD filter. The hybrid algorithm was shown to be more effective than regression or Hankel-SVD filters alone in rejecting the undesirable clutter components with single-ensemble (S-E) samples. It achieved a significant improvement in blood flow frequency estimation and estimation variance over the other competing filters.