Combined Truncated Sidelobe Suppression and Iterative Adaptive MAP Beamforming for Ultrasound Imaging

In ultrasound plane-wave imaging, the DAS beamformer, which is essentially a maximum likelihood estimator, is unable to utilize the a priori information of the signal, resulting in limited image quality, such as a wide main lobe and clutter artefacts. In this paper, based on the maximum a posteriori (MAP) framework, we present the iterative adaptive MAP method (iAMAP) beamformer. This method adaptively adjusts the output according to the signal-to-noise ratio, achieving a good trade-off between high contrast and scattering preservation performance with low complexity. Considering one drawback of the MAP framework, which struggles to eliminate high sidelobe noise, we employ a sidelobe detection framework to estimate the distribution of the sidelobe noise of the plane-wave signal. This allows for adaptive truncation of weak under-noise, avoiding subsequent erroneous suppression of the speckle background. Finally, we combine the two methods to find an optimal beamformer to obtain a trade-off between resolution and contrast, as well as speckle preservation. The results show that, compared with the delay and sum (DAS), the full width at half maximum (FWHM) of the TiAMAP for point targets was reduced by an average of 36.53% and 64.71% in simulations and experiments, respectively. The corresponding contrast ratio (CR) improved by an average of 552.38% in simulations and 348.45% in experiments. The TiAMAP method effectively suppresses clutter in the carotid artery and breast cysts, providing better imaging quality. In conclusion, the proposed method can effectively enhance image resolution and CR while maintaining low computational complexity.