Abstract
For robust blood flow imaging, filters are used to suppress undesirable noise and clutter signals. In this work, we present a higher-order singular value decomposition (HOSVD) filtering framework. This method is based on a HOSVD applied to a tensor of aperture data, with spatial, slow-time, and channel dimensions. We demonstrate that this HOSVD filtering method can outperform conventional singular value decomposition filters. Preliminary validation of this technique is shown using Field II simulations and in vivo data.
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