Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

TL;DR

This paper introduces a novel beamformer combining Eigenspace-Based Minimum Variance with Delay Multiply and Sum to enhance resolution and sidelobe suppression in photoacoustic imaging, outperforming existing methods.

Contribution

The paper proposes EIBMV-DMAS, a new beamforming approach that integrates Eigenspace-Based Minimum Variance with DMAS, significantly improving image quality in photoacoustic imaging.

Findings

EIBMV-DMAS achieves about 113 dB sidelobe reduction at 11 mm depth.

It improves SNR by approximately 75% over DMAS.

Outperforms DAS, DMAS, and EIBMV in resolution and sidelobe suppression.

Abstract

In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly used beamformer. However, it leads to a low resolution and high level of sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower sidelobes compared to DAS. In this paper, to improve the resolution and sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is shown that expanding the DMAS algebra leads to several terms which can be interpreted as DAS. Using the EIBMV adaptive beamforming instead of the existing DAS (inside the DMAS algebra expansion) is proposed to improve the image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution and sidelobes. In particular, at the depth of 11 mm of the experimental images, EIBMV-DMAS results in about 113 dB and 50 dB sidelobe reduction, compared to DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental images, the quantitative results indicate that EIBMV-DMAS leads to improvement in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and EIBMV, respectively.