Multidimensional Clutter Filtering of Aperture Domain Data for Improved Blood Flow Sensitivity.

Singular value decomposition (SVD) is a valuable factorization technique used in clutter rejection filtering for power Doppler imaging. Conventionally, SVD is applied to a Casorati matrix of radiofrequency data, which enables filtering based on spatial or temporal characteristics. In this paper, we propose a clutter filtering method that uses a higher-order singular value decomposition (HOSVD) applied to a tensor of aperture data, e.g. delayed channel data. We discuss temporal, spatial, and aperture domain features that can be leveraged in filtering and demonstrate that this multidimensional approach improves sensitivity toward blood flow. Further, we show that HOSVD remains more robust to short ensemble lengths than conventional SVD filtering. Validation of this technique is shown using Field II simulations and in vivo data.