Performance Of Minimum Variance Beamformer Research Articles

Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging

BackgroundThe minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter.MethodsIn this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation.ResultsThe proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer.ConclusionSimulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of O(N^2) has the potential to be implemented in real time using the graphics processing unit.

1Pb5-3 Improvement of performance of minimum variance beamformer by Nakagami shape parameter

1Pb5-3 Improvement of performance of minimum variance beamformer by Nakagami shape parameter

Improvement of performance of minimum variance beamformer by introducing cross covariance estimate.

The delay-and-sum beamformer is widely used in clinical ultrasound systems to obtain ultrasonic images. To improve image quality, the minimum variance (MV) beamformer was introduced in medical ultrasound imaging. The MV beamformer determines beamformer weights from ultrasonic echo signals received by individual transducer elements in an ultrasonic probe. In the present study, the MV beamformer was investigated to improve its performance. In MV beamforming, a covariance matrix of echo signals received by individual elements needs to be estimated to obtain adaptive beamformer weights. To obtain a stable estimate, a total receiving aperture is divided into subarrays, and a covariance matrix is obtained using echo signals from each subarray to average covariance matrices from all subarrays. This procedure is called "subarray averaging." In the present study, a new method for estimation of the covariance matrix was proposed. In the proposed method, a covariance matrix, namely, a cross covariance matrix, is obtained using echo signals from different subarrays. Multiple covariance matrices are obtained from all different pairs of subarrays and averaged. In the present study, the performance of the proposed method was evaluated by basic experiments on a phantom. Lateral spatial resolutions obtained by MV beamforming with conventional subarray averaging and the proposed method were similar. However, contrast obtained by MV beamforming with the proposed method was - 0.56dB, which was significantly better than the - 5.06dB obtained by MV beamforming with conventional subarray averaging. Image contrast in MV beamforming could be improved significantly by estimating "cross" covariance matrices.