Combining the APES and Minimum-variance Beamformers for Adaptive Ultrasound Imaging.

Abstract

In recent years, adaptive minimum-variance (MV) beamforming has been successfully applied to medical ultrasound imaging, resulting in simultaneous improvement in imaging resolution and contrast. MV has high resolution and hence can provide accurate estimates of the target locations. However, the MV amplitude estimates are significantly biased downward, especially when occurring the errors in model parameters. The amplitude and phase estimation (APES) beamformer gives much more accurate amplitude estimates at the target locations, but at the cost of lower resolution. To reap the benefits of both MV and APES, we have proposed a modified APES (MAPES) beamformer by adding a parameter which controls the trade-off between spatial and amplitude resolutions. We have also proposed an adaptive beamformer which combines the MV and APES. The proposed beamformer first estimates the peak locations using the MV estimator and then refines the amplitude estimates at these locations using the MAPES estimator. By using simulated and experimental data-point targets as well as cyst phantoms-we show the efficacy of the proposed beamformers.