ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2021
Multidimensional Clutter Filtering of Aperture Domain Data for Improved Blood Flow Sensitivity.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 21 citations in OpenAlex.
- Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging.Biomedical signal processing and control · 2026Article
- Intravascular ultrasound wall shear stress imaging in stented coronary arteries with ultrafast Doppler.Scientific reports · 2026Article
- High-Resolution Power Doppler Using Null Subtraction Imaging.IEEE transactions on medical imaging · 2024Article
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2 authors at 1 institution in 1 country.
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Abstract
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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