Photoacoustic Imaging using Combination of Eigenspace-Based Minimum Variance and Delay-Multiply-and-Sum Beamformers: Simulation Study

TL;DR

This study introduces EIBMV-DMAS, a novel beamforming method for photoacoustic imaging that combines eigenspace-based minimum variance with delay-multiply-and-sum, significantly enhancing image resolution and reducing sidelobes compared to existing methods.

Contribution

The paper proposes a new integrated beamforming algorithm, EIBMV-DMAS, which outperforms traditional DAS, DMAS, and EIBMV in image quality for photoacoustic imaging.

Findings

EIBMV-DMAS significantly reduces sidelobes by about 108 dB at 35 mm depth.

It improves the Signal-to-Noise Ratio by approximately 15 dB.

The method reduces the Full-Width-Half-Maximum (FWHM) by 1.65 mm.

Abstract

Delay and Sum (DAS), as the most common beamforming algorithm in Photoacoustic Imaging (PAI), having a simple implementation, results in a low-quality image. Delay Multiply and Sum (DMAS) was introduced to improve the quality of the reconstructed images using DAS. However, the resolution improvement is now well enough compared to high resolution adaptive reconstruction methods such as Eigenspace- Based Minimum Variance (EIBMV). We proposed to integrate the EIBMV inside the DMAS formula by replacing the existing DAS algebra inside the expansion of DMAS, called EIBMV-DMAS. It is shown that EIBMV-DMAS outperforms DMAS in the terms of levels of sidelobes and width of mainlobe significantly. For instance, at the depth of 35 mm, EIBMV-DMAS outperforms DMAS and EIBMV in the term of sidelobes for about 108 dB, 98 dB and 44 dB compared to DAS, DMAS, and EIBMV, respectively. The quantitative comparison has been conducted using Full-Width-Half-Maximum (FWHM) and Signal-to-Noise Ratio (SNR), and it was shown that EIBMV-DMAS reduces the FWHM about 1.65 mm and improves the SNR about 15 dB, compared to DMAS.