Low Complexity Adaptive Beamforming using Data Covariance Matrix for Ultrasound Plane Wave Imaging

Abstract

A low complexity adaptive beamformer is suggested for medical ultrasound plane wave Imaging. It represents a framework whose principle is based on the eigendecomposition (ED) of the data covariance matrix (CM) to generate the adaptive weight vectors. The proposed method is a solution of the optimization problem in which the output echo signal interference to noise ratio (eSINR) is maximized and the main objective was to reduce de computational complexity (CC) compared to minimum variance (MV) and eigenspace MV (EMV) by eliminating the CM inversion operation. The principal eigenvector associated with the maximum eigenvalue was rotated and projected onto the signal subspace to generate the adaptive weight vector. The proposed method is referred to as principal eigenvector (p-EV) beamformer and, in this work, it has been tested on phantom data from the platform Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS). A set of 21 steered plane waves was selected from a total of 75 plane waves provided by PICMUS for data processing. The spatial resolution evaluation of the proposed beamformer was performed using the Full Width at Half Maximum (FWHM) and contrast ratio (CR). We compare the proposed method using delay-and-sum (DAS), the MV, and EMV beamformers. Additionally, for all adaptive processing, we used a subarray of L=M/ 3 (M=128 elements). We found that, the proposed p-EV appears to be more effective in cyst border definition while improving the visibility of weak targets. Since in diverging wave compounding (DWC) imaging, the best image quality is by increasing the number of firing elements (FE), it is of fundamental importance to obtain a higher quality image with a lower number of FE. The CC performed by the proposed p-EV beamformer shows less time-consuming compared to MV and EMV and, under the quantitative analysis, provides improved CR and FWHM being suitable for ultrasound plane wave imaging.