Effects of Important Parameters Variations on Computing Eigenspace-Based Minimum Variance Weights for Ultrasound Tissue Harmonic Imaging

TL;DR

This paper explores how parameter variations in the eigenspace-based minimum variance (EIBMV) beamformer affect resolution and contrast in ultrasound tissue harmonic imaging, demonstrating improved image quality under noisy conditions.

Contribution

It introduces the application of EIBMV beamforming in second harmonic ultrasound imaging and analyzes the impact of key parameters on image quality.

Findings

EIBMV reduces sidelobes and enhances contrast in SHI.

Optimal parameters for EIBMV improve resolution and contrast.

EIBMV maintains high resolution even with strong noise presence.

Abstract

In recent years, the minimum variance (MV) beamforming has been widely studied due to its high resolution and contrast in B-mode Ultrasound imaging (USI). However, the performance of the MV beamformer is degraded at the presence of noise, as a result of the inaccurate covariance matrix estimation which leads to a low quality image. Second harmonic imaging (SHI) provides many advantages over the conventional pulse-echo USI, such as enhanced axial and lateral resolutions. However, the low signal-to-noise ratio (SNR) is a major problem in SHI. In this paper, Eigenspace-based minimum variance (EIBMV) beamformer has been employed for second harmonic USI. The Tissue Harmonic Imaging (THI) is achieved by Pulse Inversion (PI) technique. Using the EIBMV weights, instead of the MV ones, would lead to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer (even at the presence of a strong noise). In addition, we have investigated the effects of variations of the important parameters in computing EIBMV weights, i.e., K, L, and {\delta}, on the resolution and contrast obtained in SHI. The results are evaluated using numerical data (using point target and cyst phantoms), and the proper parameters of EIBMV are indicated for THI.