Generalized sidelobe canceler beamforming with an improved covariance matrix estimation for medical ultrasound imaging

Abstract

For adaptive beamforming, the covariance matrix estimation is a key part of the adaptive algorithm. Previously, some preprocessing algorithms must be applied to obtain an accurate covariance matrix, such as the spatial smoothing and diagonal loading. In this paper, we propose the forward-cross-backward subaperture averaging method to estimate the covariance matrix and combine it with the generalized sidelobe canceler (GSC) beamforming for the medical ultrasound imaging. This method can obtain an accurate and robust covariance matrix and overcome shortcomings of traditional preprocessing algorithms. Since the covariance matrix is in a better state, the modified beamformer can improve the quality of the echo images. We demonstrate the performance of the modified beamformer by resolving the point scatterers and cyst phantom, and compare it with the synthetic aperture beamforming (SA) and the traditional GSC beamforming. The FWHM and CR are calculated to describe the performance of the lateral resolution and contrast, respectively. The scattering points experiment shows that our method can decrease FWHM by 53.8% and 34.9% compared with SA and traditional GSC, respectively. Meanwhile, 10.2% improvement in CR is achieved compared with GSC beamforming according to the cyst phantom experiment. The results indicate that the proposed method can achieve better image quality of the system in lateral resolution and contrast.