Multidimensional Clutter Filtering of Aperture Domain Data for Improved Blood Flow Sensitivity.
Multidimensional Clutter Filtering of Aperture Domain Data for Improved Blood Flow Sensitivity.
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DOI：
10.1109/tuffc.2021.3073292
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Byram BC
中科院分区：
文献类型：
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作者：
Ozgun KA;Byram BC
奇异值分解(SVD)是一种用于功率多普勒成像杂波抑制滤波的有价值的因式分解技术。传统上，奇异值分解被应用于射频数据的卡索拉蒂矩阵，这使得能够基于空间或时间特征进行过滤。本文提出了一种将高阶奇异值分解(HOSVD)应用于孔径数据张量的杂波滤波方法，例如延迟通道数据。我们讨论了可用于过滤的时间、空间和孔径域特征，并证明这种多维方法提高了对血流的敏感性。此外，我们还表明，与传统的奇异值分解滤波相比，HOSVD对短集合长度仍然具有更强的稳健性。使用field II模拟和活体数据显示了该技术的有效性。
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.