Key result
Higher order SVD (HOSVD) applied to a tensor of aperture data improved sensitivity toward blood flow and remained more robust to short ensemble lengths than conventional SVD filtering.
Higher order SVD applied to aperture data improves blood flow sensitivity and robustness in power Doppler imaging compared to conventional SVD.
Hypothesis-generating for short-ensemble power Doppler; leaves open clinical translation beyond simulations and limited in-vivo data.
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 radio frequency data, which enables filtering based on spatial or temporal characteristics. In this article, we propose a clutter filtering method that uses a higher order SVD (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.
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Ozgun et al. (2021) studied this question. Higher order SVD (HOSVD) applied to a tensor of aperture data vs. Conventional SVD filtering was evaluated on Sensitivity toward blood flow and robustness to short ensemble lengths. Higher order SVD (HOSVD) applied to a tensor of aperture data improved sensitivity toward blood flow and remained more robust to short ensemble lengths than conventional SVD filtering.
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