Regularized Capon Beamformer Using ℓ1-Norm Applied to Photoacoustic Imaging

Delay-and-Sum (DAS), as a non-adaptive beamforming method, is one of the most common algorithms used in Photoacoustic imaging due to its simple implementation. The results obtained from this algorithm suffer from low resolution and high sidelobes. The adaptive Minimum variance (MV) method improves the image quality compared to DAS in terms of resolution and contrast. In this paper, it is proposed to add a ℓ1-norm regularization term to the conventional MV minimization problem and create a new sparse beamforming method, named Modified-Sparse-Mv (ms-Mv)algorithm. In fact, the sparsity of the output is forced to the beampattern by adding this new sparse added term, which results in more noise reduction and sidelobe suppression compared to MV. The minimization problem is convex, and therefore, it can be solved using an iterative algorithm. The results show that the proposed MS-MV method improves the signal-to-noise-ratio for about 5.36 dB and 6.44 dB compared to DAS and MV, respectively, for the designed wire phantom.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Acoustical Wavefield Imagin