Robust PCA-Based Clutter Filtering Method for Super-Resolution Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) has been established for subwavelength resolution vascular imaging and velocity mapping. However, uncorrupted detection, separation, localization and tracking of tens of microbubbles within each frame requires efficient clutter filtering methods working at low signal-to-noise-ratio (SNR). In the study, we propose to apply a robust PCA (RPCA) clutter filtering approach to deal with the challenge of microbubble extraction. A face-to-face comparison is performed between the classical SVD-based spatiotemporal filtering and the proposed RPCA method.