Short-lag spatial coherence weighted minimum variance beamformer for plane-wave images

A Spatial Coherence Approach to Minimum Variance Beamforming for Plane-Wave Compounding

IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control ◽

10.1109/tuffc.2018.2793580 ◽

2018 ◽

Vol 65 (4) ◽

pp. 522-534 ◽

Cited By ~ 16

Author(s):

Nghia Q. Nguyen ◽

Richard W. Prager

Keyword(s):

Plane Wave ◽

Spatial Coherence ◽

Minimum Variance

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High Resolution, High Contrast Beamformer Using Minimum Variance and Plane Wave Nonlinear Compounding with Low Complexity

Sensors ◽

10.3390/s21020394 ◽

2021 ◽

Vol 21 (2) ◽

pp. 394

Author(s):

Xin Yan ◽

Yanxing Qi ◽

Yinmeng Wang ◽

Yuanyuan Wang

Keyword(s):

Plane Wave ◽

Dimensional Space ◽

Contrast Ratio ◽

Low Complexity ◽

Minimum Variance ◽

Frame Rate ◽

Channel Noise ◽

Length Estimation ◽

Lower Dimensional Space

The plane wave compounding (PWC) is a promising modality to improve the imaging quality and maintain the high frame rate for ultrafast ultrasound imaging. In this paper, a novel beamforming method is proposed to achieve higher resolution and contrast with low complexity. A minimum variance (MV) weight calculated by the partial generalized sidelobe canceler is adopted to beamform the receiving array signals. The dimension reduction technique is introduced to project the data into lower dimensional space, which also contributes to a large subarray length. Estimation of multi-wave receiving covariance matrix is performed and then utilized to determine only one weight. Afterwards, a fast second-order reformulation of the delay multiply and sum (DMAS) is developed as nonlinear compounding to composite the beamforming output of multiple transmissions. Simulations, phantom, in vivo, and robustness experiments were carried out to evaluate the performance of the proposed method. Compared with the delay and sum (DAS) beamformer, the proposed method achieved 86.3% narrower main lobe width and 112% higher contrast ratio in simulations. The robustness to the channel noise of the proposed method is effectively enhanced at the same time. Furthermore, it maintains a linear computational complexity, which means that it has the potential to be implemented for real-time response.

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Signal detection using multi-channel seismic data

Bulletin of the Seismological Society of America ◽

10.1785/bssa08601a0221 ◽

1996 ◽

Vol 86 (1A) ◽

pp. 221-231 ◽

Cited By ~ 8

Author(s):

Gregory S. Wagner ◽

Thomas J. Owens

Keyword(s):

Signal Detection ◽

Seismic Data ◽

Spatial Coherence ◽

Vertical Component ◽

Principal Component ◽

Minimum Variance ◽

Data Detection ◽

Array Data ◽

Detection Approach ◽

Sample Covariance

Abstract We outline a simple signal detection approach for multi-channel seismic data. Our approach is based on the premise that the wave-field spatial coherence increases when a signal is present. A measure of spatial coherence is provided by the largest eigenvalue of the multi-channel data's sample covariance matrix. The primary advantages of this approach are its speed and simplicity. For three-component data, this approach provides a more robust statistic than particle motion polarization. For array data, this approach provides beamforming-like signal detection results without the need to form beams. This approach allows several options for the use of three-component array data. Detection statistics for three-component, vertical-component array, and three different three-component array approaches are compared to conventional and minimum-variance vertical-component beamforming. Problems inherent in principal-component analysis (PCA) in general and PCA of high-frequency seismic data in particular are also discussed. Multi-channel beamforming and the differences between principal component and factor analysis are discussed in the appendix.

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