Artifact reduction using minimum variance-based sparse subarray technique in linear-array photoacoustic tomography

In linear-array photoacoustic imaging, different types of algorithms and beamformers are used to construct the images. Delay-and-Sum (DAS), as a non-adaptive algorithm, is one of the most popular algorithms used due to its low complexity. However, the results obtained from this algorithm contain high sidelobes and wide mainlobe. The adaptive Minimum Variance (MV) beamformer can address these limitations and improve the images in terms of resolution and contrast. In this paper, it is proposed to suppress the sidelobes more efficiently compared to MV by eliminating the effect of the samples caused by noise and interference. This would be achieved by zeroing the samples corresponding to the lower values of the calculated weights. In the other words, in the proposed MV-based-sparse subarray (MVB-S) method, the subarrays are considered to be sparse. The results show that MVB-S method leads to signal-to-noise-ratio improvement about 39.72 dB and 18.92 dB in average, compared to DAS and MV, respectively, which indicates the good performance of MVB-S method in noise reduction and sidelobe suppression.