Randomized Spatial Downsampling-Based Cauchy-RPCA Clutter Filtering for High-Resolution Ultrafast Ultrasound Microvasculature Imaging and Functional Imaging
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control(2022)
摘要
Effective tissue clutter filtering and noise removing are essential for ultrafast Doppler imaging. Singular vector decomposition (SVD)-based spatiotemporal method has been applied as a classical method to remove the clutter and strong motion artifacts. However, performance of the SVD-based methods often depends on a proper eigenvector thresholding, i.e., the separation of signal subspaces of small-value blood flow, large-value static tissue, and noise. In the study, a Cauchy-norm-based robust principal component analysis (Cauchy-RPCA) method is developed via Cauchy-norm-based sparsity penalization, which enhances the blood flow extraction of small-vessels. A randomized spatial downsampling strategy and alternating direction method of multipliers (ADMM) are further involved to accelerate the computation. A face-to-face comparison is carried out among the classical SVD, traditional RPCA, blind deconvolution-based RPCA (BD-RPCA), and the proposed Cauchy-RPCA methods. Ultrafast ultrasound imaging dataset recorded from rat brain is used to investigate the performance of the proposed Cauchy-RPCA method in terms of clutter filtering, power Doppler, color Doppler, and functional ultrasound (fUS) imaging. The computational efficiency is finally discussed.
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关键词
Animals,Blood Flow Velocity,Image Processing, Computer-Assisted,Microvessels,Phantoms, Imaging,Rats,Signal Processing, Computer-Assisted,Ultrasonography
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