Anomaly Detection and Suppression for Improved Plane-Wave Ultrasound Imaging Using Minimum Variance Beamformer

In recent years, with the continuously increasing demands for temporal and spatial resolution, traditional line-scanning mode ultrasound imaging technology has gradually revealed its limitations when addressing new ultrasound imaging modalities. To overcome these challenges, ultrafast ultrasound imaging has emerged as a research frontier due to its high frame rate. However, while plane-wave imaging significantly improves temporal resolution, its image quality is compromised by side lobe effects, leading to reduced contrast and increased artifacts. These issues primarily stem from the inability to achieve ideal point focusing during plane wave emission, resulting in signal interference from non-target regions. To address these challenges, this study proposes a Minimum Variance Beamformer integrated with the Circular Reflection Anomaly Detection and Suppression Algorithm (CRADS), designed to mitigate side lobe artifacts in plane-wave ultrasound imaging. By integrating delay-compensated analysis of circular tissue reflection regions and mismatch filter optimization, the algorithm identifies and suppresses anomalies in the amplitude and spatial distribution of echo signals. Experimental results demonstrate that CRADS significantly outperforms DAS and MV algorithms in spatial resolution, contrast recovery, and contrast-to-noise ratio, effectively enhancing imaging quality for both liquid and solid cyst models.