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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 witha wide mainlobe and high level of sidelobes. Minimum variance (MV), as anadaptive beamformer, overcomes these limitations and improves the imageresolution and contrast. In this paper, a novel algorithm, namedmodified-sparse-MV (MS-MV) is proposed in which a L1-norm constraint is addedto the MV minimization problem after some modifications, in order to suppressthe sidelobes more efficiently, compared to MV. The added constraint can beinterpreted as the sparsity of the output of the MV beamformed signals. Sincethe final minimization problem is convex, it can be solved efficiently using asimple iterative algorithm. The numerical results show that the proposedmethod, MS-MV beamformer, improves the signal-to-noise (SNR) about 19.48 dB, inaverage, compared to MV. Also, the experimental results, using a wire-targetphantom, show that MS-MV leads to SNR improvement of about 2.64 dB incomparison with the MV.

Photoacoustic Image Formation Based on Sparse Regularization of Minimum Variance Beamformer