Abstract
BackgroundThe Eigenspace-based beamformers, by orthogonal projection of signal subspace, can remove a large part of the noise, and provide better imaging contrast upon the minimum variance beamformer. However, wrong estimate of signal and noise component may bring dark-spot artifacts and distort the signal intensity. The signal component and noise and interference components are considered uncorrelated in conventional eigenspace-based beamforming methods. In ultrasound imaging, however, signal and noise are highly correlated. Therefore, the oblique projection instead of orthogonal projection should be taken into account in the denoising procedure of eigenspace-based beamforming algorithm.MethodsIn this paper, we propose a novel eigenspace-based beamformer based on the oblique subspace projection that allows for consideration of the signal and noise correlation. Signal-to-interference-pulse-noise ratio and an eigen-decomposing scheme are investigated to propose a new signal and noise subspaces identification. To calculate the beamformer weights, the minimum variance weight vector is projected onto the signal subspace along the noise subspace via an oblique projection matrix.ResultsWe have assessed the performance of proposed beamformer by using both simulated software and real data from Verasonics system. The results have exhibited the improved imaging qualities of the proposed beamformer in terms of imaging resolution, speckle preservation, imaging contrast, and dynamic range.ConclusionsResults have shown that, in ultrasound imaging, oblique projection is more sensible and effective than orthogonal subspace projection. Better signal and speckle preservation could be obtained by oblique projection compare to orthogonal projection. Also shadowing artifacts around the hyperechoic targets have been eliminated. Implementation the new subspace identification has enhanced the imaging resolution of the minimum variance beamformer due to the increasing the signal power in direction of arrival. Also it has offered better sidelobe suppression and a higher dynamic range.
Highlights
Beamforming is a signal processing technique that enhances image quality by applying appropriate gains and delays to echo signals
Most adaptive beamformers are based on the linear constraint minimum variance (LCMV) method that was proposed by Capon in 1969 [1]
Since the one-steering-angle plane-wave imaging suffered from low SNR [23], we have evaluated the performance of proposed method in this worse case
Summary
Beamforming is a signal processing technique that enhances image quality by applying appropriate gains and delays to echo signals. As a non-adaptive beamforming algorithm, DAS provides low imaging quality in contrast and resolution. The minimum variance (MV) and eigenspace-based methods are representative methods in adaptive beamformers. Most adaptive beamformers are based on the linear constraint minimum variance (LCMV) method that was proposed by Capon in 1969 [1]. MV beamformer performance correlates to the signal-to-noise-ratio (SNR) of the signal. Mohammadzadeh et al [6] utilized the forward–backward minimum variance beamforming to enhance the covariance matrix estimation robustness without comprising imaging resolution. The Eigenspace-based beamformers, by orthogonal projection of signal subspace, can remove a large part of the noise, and provide better imaging contrast upon the minimum variance beamformer. The signal component and noise and interference components are considered uncorrelated in conventional eigenspace-based beamforming methods. The oblique projection instead of orthogonal projection should be taken into account in the denoising procedure of eigenspace-based beamforming algorithm
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