Image Improvement in Linear-Array Photoacoustic Imaging using High Resolution Coherence Factor Weighting Technique

TL;DR

This paper introduces a high-resolution coherence factor weighting technique for linear-array photoacoustic imaging that significantly enhances image quality by reducing sidelobes and improving resolution and contrast.

Contribution

It proposes a novel HRCF weighting method replacing DAS with MV in the coherence factor formula, demonstrating superior imaging performance.

Findings

HRCF improves FWHM by 91% at 40mm depth.

HRCF increases SNR by 40% at 40mm depth.

HRCF enhances contrast ratio by 62% at 20mm depth.

Abstract

In Photoacoustic imaging (PAI), the most prevalent beamforming algorithm is delay-and-sum (DAS) due to its simple implementation. However, it results in a low quality image affected by the high level of sidelobes. Coherence factor (CF) can be used to address the sidelobes in the reconstructed images by DAS, but the resolution improvement is not good enough compared to the high resolution beamformers such as minimum variance (MV). As a weighting algorithm in linear-array PAI, it was proposed to use high-resolution-CF (HRCF) weighting technique in which MV is used instead of the existing DAS in the formula of the conventional CF. The higher performance of HRCF was proved numerically and experimentally. The quantitative results obtained with the simulations show that at the depth of 40 mm, in comparison with DAS+CF and MV+CF, HRCF improves the full-width-half-maximum of about 91 % and 15 % and the signal-to-noise ratio about 40 % and 14 %, respectively. Moreover, the contrast ratio at the depth of 20 mm has been improved about 62 % and 21 % by HRCF, compared to DAS+CF and MV+CF, respectively