Low-complexity adaptive beamforming using autocorrelation-based generalized coherence factor

Abstract

Ultrasound focusing quality can be adaptively estimated by calculating generalized coherence factor (GCF) from channel data and each image pixel is weighted by GCF to suppress sidelobe artifacts. Conventionally, Fourier Transform (FFT) is utilized to determine the full channel spectrum and thus suffers from high computation load. In this work, autocorrelation (AR)-based algorithm is utilized to provide the spectral parameters of channel data for GCF estimation with reduced complexity. Autocorrelation takes advantage of the phase difference among neighboring channel pairs to estimate the mean frequency and bandwidth of channel spectrum. Based on these spectral parameters, GCF weighting can be approximated by assuming normalized distribution (e.g., Gaussian) of the channel spectrum. Since the spatial frequency in channel spectrum actually corresponds to the incident angle of echoes, the spectral power ratio within the defined range of mainlobe direction can be analytically computed from the simulated spectral shape as the value of GCF weighting. While the GCF computation complexity of a N-channel system reduces from O(NlogN) with FFT to O(N) with AR, results shows that the image contrast increases from 6.7 with GCF-FFT to 9.0 with GCF-AR. Corresponding B-mode images also indicate that the speckle background in GCF-AR image is more resistant to the black-region artifacts in the presence of strong reflectors. Overall, GCF-AR can improve the computation efficiency of adaptive imaging while providing superior image quality.