A Low-complexity Minimum-variance Beamformer Based on Orthogonal Decomposition of the Compounded Subspace.

In this study, we propose a novel MV beamformer based on orthogonal decomposition of the compounded subspace (CS) of the covariance matrix in synthetic aperture (SA) imaging, which aims to reduce the dimensions of the covariance matrix and therefore reduce the computational complexity. Multiwave spatial smoothing is applied to the echo signals for the accurate estimation of the covariance matrix, and adaptive weight vectors are calculated from the low-dimensional subspace of the original covariance matrix. We conducted simulation, experimental and in vivo studies to verify the performance of the proposed method. The results indicate that the proposed method performs well in maintaining the advantage of high spatial resolution and effectively reduces the computational complexity compared with the standard MV beamformer. In addition, the proposed method shows good robustness against sound velocity errors.
PMID: 33355519 [PubMed - in process]

Source: Ultrasonic Imaging - December 30, 2020 Category: Radiology Tags: Ultrason Imaging Source Type: research