Robust Beamforming for Ultrasonic Imaging using Wavelet-based Thresholding and Coherence Weighting

Abstract

The delay-and-sum (DS) beamforming and delay-weight-and-sum (DWS) beamforming are primary methods in ultrasonic imaging with phased arrays. Total focusing method (TFM) and Minimum Variance (MV) based adaptive beamforming are well-known methods within DS and DWS beamforming, respectively. The MV-based adaptive beamforming significantly reduces interferences and provides high-resolution image compared to TFM beamforming, at the cost of high computational complexity, and sensitivity to input statistics for matrix inversion. To address these challenges, recently, iterative MV (IMV) has been proposed to alleviate computational burdens without the need for matrix inversion. The delay-multiply-and-sum (DMAS) beamforming enhances TFM beamforming performance by employing spatial information of the array signals. However, the resulting images remain susceptible to speckle and background noises in all of these beamformers. In this paper, we aim to improve these beamforming methods so that speckle and Gaussian background noise are reduced while preserving the quality of the reflective echoes in ultrasonic images. In the proposed method, a wavelet transform with a novel threshold function is applied to the received signals to initially reduce the noise, followed by the application of the beamformer. Subsequently, the coherence weighting using the denoised signals is derived, and the obtained coherence weighting is then integrated into the beamforming process. Simulation results demonstrate that the proposed method achieves a significant reduction in background noise and speckles of the above beamformer, and particularly reduces background noise and speckles of beamformer up to approximately -27dB while preserving the detection capability of reflective points.

Keywords

• coherence weighting
• covariance matrix
• delay-and-sum beamformer
• mv beamformer
• ultrasonic waves