Linear-Array Photoacoustic Imaging Using Minimum Variance-Based Delay Multiply and Sum Adaptive Beamforming Algorithm

TL;DR

This paper introduces a novel beamforming algorithm combining Minimum Variance adaptive beamforming with Delay-Multiply-and-Sum to significantly improve resolution and reduce sidelobes in photoacoustic imaging.

Contribution

The paper proposes the MVB-DMAS algorithm, integrating MV adaptive beamforming with DMAS, achieving superior resolution and sidelobe suppression in photoacoustic imaging.

Findings

MVB-DMAS reduces sidelobes by up to 31 dB at 45 mm depth.

It improves full-width-half-maximum by approximately 96%.

It enhances signal-to-noise ratio by about 89%.

Abstract

In Photoacoustic imaging (PA), Delay-and-Sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely Delay-Multiply-and-Sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a novel beamformer is introduced using Minimum Variance (MV) adaptive beamforming combined with DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31 dB, 18 dB and 8 dB sidelobes reduction compared to DAS, MV and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96 %, 94 % and 45 % and signal-to-noise ratio about 89 %, 15 % and 35 % compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.