Short-lag spatial coherence combined with eigenspace-based minimum variance beamformer for synthetic aperture ultrasound imaging

Recently, short-lag spatial coherence (SLSC) imaging has been widely studied to improve image contrast and contrast-to-noise ratio (CNR) in ultrasound imaging. Nevertheless, SLSC is unable to provide a good imaging resolution. Eigenspace-based minimum variance (ESBMV) beamformer was previously devised to promote imaging resolution, while enhancing imaging contrast. However, ESBMV will cause black-spot artifact problem under a high threshold of eigenvalues. Given their complementary properties, in this study, we propose an imaging method with synthetic aperture (SA) ultrasound imaging by combining SLSC weighting (SLSCw) and ESBMV, to improve imaging quality at all depth. Based on the spatial coherence of different sources, adaptive threshold of eigenvalues is designed for ESBMV. In the proposed method, receive aperture data are directly summed to get the receive aperture synthesized data, and then SLSC and ESBMV are applied in transmit aperture based on the receive synthesized data. After that, the estimated SLSC value is adopted as a weighting factor for ESBMV beamforming output. We demonstrate the performance of the proposed method based on simulated and experimental data. The results show that the proposed method can not only achieve satisfactory improvement in resolution and contrast but also remove the black-spot artifacts.