📝 SummarXiv

Abstract

High-sensitivity clutter filtering is a fundamental step in ultrasound microvascular imaging. Singular value decomposition (SVD) and robust principal component analysis (rPCA) are the main clutter filtering strategies. However, both strategies are limited in feature modeling and tissue-blood flow separation for high-quality microvascular imaging. Recently, deep learning-based clutter filtering has shown potential in more thoroughly separating tissue and blood flow signals. However, the existing supervised filters face the challenges of interpretability and lack of in-vitro and in-vivo ground truths. While the interpretability issue can be addressed by algorithm deep unfolding, the training ground truth remains unsolved. To this end, this paper proposes an unsupervised unfolded rPCA (U2-rPCA) method that preserves mathematical interpretability and is insusceptible to learning labels. Specifically, U2-rPCA is unfolded from an iteratively reweighted least squares (IRLS) rPCA baseline with intrinsic low-rank and sparse regularization. A sparse-enhancement unit is added to the network to strengthen its capability to capture the sparse micro-flow signals. U2-rPCA is like an adaptive filter that is trained with part of the image sequence and then used for the following frames. Experimental validations on a in-silico dataset and public in-vivo datasets demonstrated the outperformance of U2-rPCA when compared with the SVD-based method, the rPCA baseline, and another deep learning-based filter. Particularly, the proposed method improved the contrastto-noise ratio (CNR) of the power Doppler image by 2 dB to 10 dB when compared with other methods. Furthermore, the effectiveness of the building modules of U2-rPCA was validated through ablation studies.