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Delay-and-Sum (DAS) is the most common algorithm used in photoacoustic (PA) image formation. However, this algorithm results in a reconstructed image with a wide mainlobe and high level of sidelobes. Minimum variance (MV), as an adaptive beamformer, overcomes these limitations and improves the image resolution and contrast. In this paper, a novel algorithm, named modified-sparse-MV (MS-MV) is proposed in which a L1-norm constraint is added to the MV minimization problem after some modifications, in order to suppress the sidelobes more efficiently, compared to MV. The added constraint can be interpreted as the sparsity of the output of the MV beamformed signals. Since the final minimization problem is convex, it can be solved efficiently using a simple iterative algorithm. The numerical results show that the proposed method, MS-MV beamformer, improves the signal-to-noise (SNR) about 19.48 dB, in average, compared to MV. Also, the experimental results, using a wire-target phantom, show that MS-MV leads to SNR improvement of about 2.64 dB in comparison with the MV.

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, Comment: arXiv admin note: text overlap with arXiv:1709.07965

Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound (US) imaging and the contrast of pure optical imaging. For linear-array PAI, a beamformer has to be used as the reconstruction algorithm. Delay-and-sum (DAS) is the most prevalent beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images along with significant effects of the off-axis signals. Coherence factor (CF) is a weighting method in which each pixel of the reconstructed image is weighted, based on the spatial spectrum of the aperture, to improve the contrast. In this paper, it has been shown that the numerator of the formula of CF contains a DAS algebra, and it was proposed to use the delay-multiply-and-sum (DMAS) beamformer instead of the available DAS on the numerator. The proposed weighting technique, modified CF (MCF), has been evaluated numerically and experimentally compared to CF, and it was shown that MCF leads to lower sidelobes and better detectable targets. The quantitative results of the experiment (using wire targets) show that MCF leads to for about 45% and 40% improvement, in comparison with CF, in the terms of signal-to-noise ratio and full-width-half-maximum, respectively., Comment: This manuscript is accepted in Journal of Biomedical Optics (JBO). The second edition contains Ex Vivo experimental results