LAUSR

Adaptive beamforming has been widely studied for ultrasound imaging over the past few decades. The minimum variance (MV) and generalized coherence factor (GCF) approaches have been validated as effective methods. However, the MV method had a limited contribution to contrast improvement, while the GCF method suffered from severe speckle distortion in previous studies. In this article, a novel ultrasound beamforming approach based on MV and GCF beamformers is proposed to enhance the spatial resolution and contrast in synthetic aperture (SA) ultrasound imaging. First, the MV optimization problem is conceptually redefined by minimizing the total power of the transmitting and receiving outputs. Estimation of the covariance matrices in transmit and receive apertures is carried out and then utilized to determine adaptive weighting vectors. Second, a data-compounding method, viewed as a spatial low-pass filter, is introduced to the GCF method to optimize the spatial spectrum of echo signals and obtain better performance. Robust principal component analysis (RPCA) processing is additionally employed to obtain the final output. Simulation, experimental, and in vivo studies are conducted on different data sets. Relative to the traditional delay-and-sum (DAS) beamformer, mean improvements in the full-width at half-maximum and contrast ratio of 89% and 94%, respectively, are achieved. Thus, considerable enhancement of the spatial resolution and contrast is obtained by the proposed method. Moreover, the proposed method performs better in terms of the computational complexity. In summary, the proposed scheme effectively enhances ultrasound imaging quality.