  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.
