An Adaptive Eigenspace-based Beamformer Using Coherence Factor In Ultrasound Imaging

Since the minimum variance beamformer occurred, adaptive beamformers in ultrasound imaging have been widely studied. Eigenspace-based minimum variance beamformer is an outstanding method which utilizes eigenvalue decomposition to construct signal and noise subspaces, enhancing the contrast of minimum variance beamformer. However, due to the constant threshold by which signal and noise subspaces are separated, the image will be distorted even if its contrast is improved. In this paper, a relationship between the eigenvalue threshold and the coherence factor (CF) is established to adjust the threshold adaptively so that the contrast is retained and the distortion is alleviated. Simulated and experimental data are used to reconstruct the image. Results of the proposed method are compared with results of the eigenspace-based minimum variance beamformer, which proves the validity of the proposed method.