Contrast-Free Ultrasound Microvascular Imaging With Optimal Clutter Shrinkage to Enhance Tumor Vascular Quantification

Singular value decomposition (SVD) clutter filtering is an effective method to improve microvessel detection in contrast-free ultrasound. In standard SVD filters, the singular values below the blood-clutter rank threshold are set to zero. This approach discards any blood signal power present in the low-rank singular vectors and finding the correct blood-clutter threshold remains challenging. In this study, we demonstrate that an SVD clutter filter using the Gavish-Donoho method for optimal shrinkage, i.e., reweighting, of low-rank singular values, improves the detection of fine microvascular details compared to a standard SVD clutter filter using a hard threshold.