Mutual Coherence for The Enhancement of Minimum Variance Beamforming

As an adaptive beamforming method, minimum variance beamforming (MV) has been introduced to medical ultrasound imaging to improve the image quality in terms of spatial resolution and contrast resolution. Different from the conventional delay-and-sum (DAS) with a pre-determined apodization, MV is capable of adaptively producing apodization pixel-by-pixel based on the incoming echoes. The key step of MV is estimating the covariance matrix from the incoming echoes. The most common approach for covariance matrix estimation is averaging the self-coherences of the incoming echoes from different sources. The sources may be subarrays, sampling time or transmit events. In this work, we calculate the covariance matrix by weighting summation the self- and mutual-coherences of incoming echoes from different transmissions to improve the image quality of coherent compounding imaging methods. The proposed MV method is validated through phantom and in vivo experiments with linear and convex arrays. The results demonstrate that the proposed method offers higher lateral resolution, finer speckle pattern and higher contrast in both phantom and in-vivo experiments.