Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues

Minimum variance (MV) based beamforming techniques have been successfully applied to medical ultrasound imaging. These adaptive methods offer higher lateral resolution, lower sidelobes, and better definition of edges compared to delay and sum beamforming (DAS). In standard medical ultrasound, the bone surface is often visualized poorly, and the boundaries region appears unclear. This may happen due to fundamental limitations of the DAS beamformer, and different artifacts due to, e.g., specular reflection, and shadowing. The latter can degrade the robustness of the MV beamformers as the statistics across the imaging aperture is violated because of the obstruction of the imaging beams. In this study, we employ forward/backward averaging to improve the robustness of the MV beamforming techniques. Further, we use an eigen-spaced minimum variance technique (ESMV) to enhance the edge detection of hard tissues. In simulation, in vitro, and in vivo studies, we show that performance of the ESMV beamformer depends on estimation of the signal subspace rank. The lower ranks of the signal subspace can enhance edges and reduce noise in ultrasound images but the speckle pattern can be distorted.