Eigenspace-Based Minimum Variance Adaptive Beamformer Combined with Delay Multiply and Sum: Experimental Study

TL;DR

This study introduces EIBMV-DMAS, a novel beamforming algorithm combining eigenspace-based minimum variance with delay multiply and sum, significantly enhancing image resolution and reducing sidelobes in linear-array photoacoustic imaging.

Contribution

The paper presents the first combination of EIBMV with DMAS for photoacoustic imaging, demonstrating superior performance over existing methods in resolution and sidelobe suppression.

Findings

Degrades sidelobes by up to 365% compared to DAS.

Improves SNR by approximately 158%.

Outperforms DAS, DMAS, and EIBMV in experimental tests.

Abstract

Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.