Adaptive spatial smoothing-based minimum variance beamforming using signal coherence to improve image quality

The classic minimum variance (MV) beamforming can significantly improve ultrasound image resolution through the minimum output power of each aperture based on the optimum weight vector. In the MV beamformer, spatial smoothing and diagonal loading are commonly used to reduce errors and increase robustness. Although the increased length of the subarray leads to a better resolution performance, the robustness decreases with degraded speckle quality. Thus, it is difficult to achieve excellent speckle quality and resolution performance simultaneously due to the fixed subarray length in spatial smoothing. This paper aims to maintain resolution performance and improve the speckle quality by adaptively determining the subarray lengths for spatial smoothing. To precisely validate the performance of the proposed MV with adaptive spatial smoothing technique (ASS-MV), a simulated phantom and a public dataset geabr_0 was used in this paper. Compared with MV in the experimental phantom study, the ASS-MV method improves the generalized contrast-to-noise ratio from 0.07 to 0.89, improves the contrast ratio from 12.6 dB to 19.7 dB, and improves the speckle signal-to-noise ratio from 1.42 to 2.31, while simultaneously retain the resolution performance of MV.