Artifact reduction using minimum variance-based sparse subarray technique in linear-array photoacoustic tomography

Roya Paridar; Moein Mozaffarzadeh; Mohammad Mehrmohammadi; Maryam Basij; Mahdi Orooji

doi:10.1117/12.2508004

27 February 2019 Artifact reduction using minimum variance-based sparse subarray technique in linear-array photoacoustic tomography

Roya Paridar, Moein Mozaffarzadeh, Mohammad Mehrmohammadi, Maryam Basij, Mahdi Orooji

Author Affiliations +

Proceedings Volume 10878, Photons Plus Ultrasound: Imaging and Sensing 2019; 108786M (2019) https://doi.org/10.1117/12.2508004
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

In linear-array photoacoustic imaging, different types of algorithms and beamformers are used to construct the images. Delay-and-Sum (DAS), as a non-adaptive algorithm, is one of the most popular algorithms used due to its low complexity. However, the results obtained from this algorithm contain high sidelobes and wide mainlobe. The adaptive Minimum Variance (MV) beamformer can address these limitations and improve the images in terms of resolution and contrast. In this paper, it is proposed to suppress the sidelobes more efficiently compared to MV by eliminating the effect of the samples caused by noise and interference. This would be achieved by zeroing the samples corresponding to the lower values of the calculated weights. In the other words, in the proposed MV-based-sparse subarray (MVB-S) method, the subarrays are considered to be sparse. The results show that MVB-S method leads to signal-to-noise-ratio improvement about 39.72 dB and 18.92 dB in average, compared to DAS and MV, respectively, which indicates the good performance of MVB-S method in noise reduction and sidelobe suppression.

Citation Download Citation

Roya Paridar, Moein Mozaffarzadeh, Mohammad Mehrmohammadi, Maryam Basij, and Mahdi Orooji "Artifact reduction using minimum variance-based sparse subarray technique in linear-array photoacoustic tomography", Proc. SPIE 10878, Photons Plus Ultrasound: Imaging and Sensing 2019, 108786M (27 February 2019); https://doi.org/10.1117/12.2508004

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KEYWORDS

Phased arrays

Signal to noise ratio

Reconstruction algorithms

Sensors

Image resolution

Photoacoustic tomography

Denoising

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