Sparsity-based beamforming to enhance two-dimensional linear-array photoacoustic tomography

Roya Paridar; Moein Mozaffarzadeh; Vijitha Periyasamy; Manojit Pramanik; Mohammad Mehrmohammadi; Mahdi Orooji

doi:10.1016/j.ultras.2019.03.010

Sparsity-based beamforming to enhance two-dimensional linear-array photoacoustic tomography

Ultrasonics. 2019 Jul:96:55-63. doi: 10.1016/j.ultras.2019.03.010. Epub 2019 Mar 15.

Authors

Roya Paridar¹, Moein Mozaffarzadeh¹, Vijitha Periyasamy², Manojit Pramanik², Mohammad Mehrmohammadi³, Mahdi Orooji⁴

Affiliations

¹ Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran.
² School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore.
³ Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA.
⁴ Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran. Electronic address: morooji@modares.ac.ir.

PMID: 31005780
DOI: 10.1016/j.ultras.2019.03.010

Abstract

In linear-array photoacoustic imaging (PAI), beamforming methods can be used to reconstruct the images. Delay-and-sum (DAS) beamformer is extensively used due to its simple implementation. However, this algorithm results in high level of sidelobes and low resolution. In this paper, it is proposed to form the photoacoustic (PA) images through a regularized inverse problem to address these limitations and improve the image quality. We define a forward/backward problem of the beamforming and solve the inverse problem using a sparse constraint added to the model which forces the sparsity of the output beamformed data. It is shown that the proposed Sparse beamforming (SB) method is robust against noise due to the sparsity nature of the problem. Numerical results show that the SB method improves the signal-to-noise ratio (SNR) for about 98.69 dB, 82.26 dB and 74.73 dB, in average, compared to DAS, delay-multiply-and-sum (DMAS) and double stage-DMAS (DS-DMAS), respectively. Also, quantitative evaluation of the experimental results shows a significant noise reduction using SB algorithm. In particular, the contrast ratio of the wire phantom at the depth of 30 mm is improved about 103.97 dB, 82.16 dB and 65.77 dB compared to DAS, DMAS and DS-DMAS algorithms, respectively, indicating a better performance of the proposed SB in terms of noise reduction.

Keywords: -norm regularization.; Beamforming; Modelling; Photoacoustic imaging; Sparsity.