Double Minimum Variance Beamforming Method to Enhance Photoacoustic Imaging

Abstract

One of the common algorithms used to reconstruct photoacoustic (PA) images is the non-adaptive Delay-and-Sum (DAS) beamformer. However, the quality of the reconstructed PA images obtained by DAS is not satisfying due to its high level of sidelobes and wide mainlobe. In contrast, adaptive beamformers, such as minimum variance (MV), result in an improved image compared to DAS. In this paper, a novel beamforming method, called Double MV (D-MV) is proposed to enhance the image quality compared to the MV. It is shown that there is a summation procedure between the weighted subarrays in the output of the MV beamformer. This summation can be interpreted as the non-adaptive DAS beamformer. It is proposed to replace the existing DAS with the MV algorithm to reduce the contribution of the off-axis signals caused by the DAS beamformer between the weighted subarrays. The numerical results show that the proposed technique improves the full-width-half-maximum (FWHM) and signal-to-noise ratio (SNR) for about 28.83 \mu m and 4.8 dB in average, respectively, compared to MV beamformer. Also, quantitative evaluation of the experimental results indicates that the proposed D-MV leads to 0.15 mm and 1.96 dB improvement in FWHM and SNR, in comparison with MV beamformer.