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

摘要

Delay and sum (DAS) is the most common beamforming algorithm in linear-arrayphotoacoustic 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 imagequality. However, the resolution improvement is not well enough compared toeigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformerhas been combined with DMAS algebra, called EIBMV-DMAS, using the expansion ofDMAS algorithm. The proposed method is used as the reconstruction algorithm inlinear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitativeand qualitative results show that it outperforms DAS, DMAS and EIBMV. Theproposed method degrades the sidelobes for about 365 %, 221 % and 40 %,compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improvesthe SNR about 158 %, 63 % and 20 %, respectively.