Spatio-Temporal Oriented Gradient (STOG) Filtering for Ultrasound Localization Microscopy: Preserving Slow and Fast Flow Components

Ultrasound Localization Microscopy (ULM) enables super-resolution vascular imaging but depends heavily on clutter filtering. Conventional Singular Value Decomposition (SVD) struggles to distinguish slow microvascular flows from static tissue, often suppressing diagnostically important signals. We propose a Spatio-Temporal Oriented Gradient (STOG) filter that exploits pixelwise gradient features in space and time. By combining co-occurrence and temporal elevation analysis, STOG separates both fast and slow flow components. Experiments with flow phantom and in vivo rabbit data demonstrated that STOG preserves microvascular structures missed by SVD, while maintaining overall vascular patterns. Despite minor tissue artifacts, STOG suggests a promising direction for gradient-based clutter filtering in angiogenesis imaging relevant to ischemic stroke prognosis.