Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging

Photoacoustic (PA) imaging combining the advantages of high resolution of ultrasound imaging and high contrast of optical imaging provides images with good quality. PA imaging often suffers from disadvantages such as clutter noises and decreased signal-to-noise-ratio at higher depths. One studied me...

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Main Authors: Shamekhi, Sadaf, Periyasamy, Vijitha, Pramanik, Manojit, Mehrmohammadi, Mohammad, Mohammadzadeh Asl, Babak
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146482
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1464822023-12-29T06:54:43Z Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging Shamekhi, Sadaf Periyasamy, Vijitha Pramanik, Manojit Mehrmohammadi, Mohammad Mohammadzadeh Asl, Babak School of Chemical and Biomedical Engineering Engineering::Bioengineering Photoacoustic Imaging Adaptive Beamforming Photoacoustic (PA) imaging combining the advantages of high resolution of ultrasound imaging and high contrast of optical imaging provides images with good quality. PA imaging often suffers from disadvantages such as clutter noises and decreased signal-to-noise-ratio at higher depths. One studied method to reduce clutter noises is to use weighting factors such as coherence factor (CF) and its modified versions that improve resolution and contrast of images. In this study, we combined the Eigen-space based minimum variance (EIBMV) beamformer with the sign coherence factor (SCF) and show the ability of these methods for noise reduction when they are used in combination with each other. In addition, we compared the proposed method with delay-and-sum (DAS) and minimum variance (MV) beamformers in simulated and experimental studies. The simulation results show that the proposed EIBMV-SCF method improves the SNR about 94 dB, 87.65 dB, and 62.29 dB compared to the DAS, MV, and EIBMV, respectively, and the corresponding improvements were 79.37/34.43 dB, 77.25/26.96 dB, and 33.19/25.56 dB in the ex vivo/in vivo experiments. Accepted version 2021-02-18T07:38:52Z 2021-02-18T07:38:52Z 2020 Journal Article Shamekhi, S., Periyasamy, V., Pramanik, M., Mehrmohammadi, M., & Mohammadzadeh Asl, B. (2020). Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging. Ultrasonics, 108, 106174-. doi:10.1016/j.ultras.2020.106174 0041-624X https://hdl.handle.net/10356/146482 10.1016/j.ultras.2020.106174 32502893 2-s2.0-85085648478 108 106174 en Ultrasonics © 2020 Elsevier B.V. All rights reserved. This paper was published in Ultrasonics and is made available with permission of Elsevier B.V. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Bioengineering
Photoacoustic Imaging
Adaptive Beamforming
spellingShingle Engineering::Bioengineering
Photoacoustic Imaging
Adaptive Beamforming
Shamekhi, Sadaf
Periyasamy, Vijitha
Pramanik, Manojit
Mehrmohammadi, Mohammad
Mohammadzadeh Asl, Babak
Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging
description Photoacoustic (PA) imaging combining the advantages of high resolution of ultrasound imaging and high contrast of optical imaging provides images with good quality. PA imaging often suffers from disadvantages such as clutter noises and decreased signal-to-noise-ratio at higher depths. One studied method to reduce clutter noises is to use weighting factors such as coherence factor (CF) and its modified versions that improve resolution and contrast of images. In this study, we combined the Eigen-space based minimum variance (EIBMV) beamformer with the sign coherence factor (SCF) and show the ability of these methods for noise reduction when they are used in combination with each other. In addition, we compared the proposed method with delay-and-sum (DAS) and minimum variance (MV) beamformers in simulated and experimental studies. The simulation results show that the proposed EIBMV-SCF method improves the SNR about 94 dB, 87.65 dB, and 62.29 dB compared to the DAS, MV, and EIBMV, respectively, and the corresponding improvements were 79.37/34.43 dB, 77.25/26.96 dB, and 33.19/25.56 dB in the ex vivo/in vivo experiments.
author2 School of Chemical and Biomedical Engineering
author_facet School of Chemical and Biomedical Engineering
Shamekhi, Sadaf
Periyasamy, Vijitha
Pramanik, Manojit
Mehrmohammadi, Mohammad
Mohammadzadeh Asl, Babak
format Article
author Shamekhi, Sadaf
Periyasamy, Vijitha
Pramanik, Manojit
Mehrmohammadi, Mohammad
Mohammadzadeh Asl, Babak
author_sort Shamekhi, Sadaf
title Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging
title_short Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging
title_full Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging
title_fullStr Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging
title_full_unstemmed Eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging
title_sort eigenspace-based minimum variance beamformer combined with sign coherence factor : application to linear-array photoacoustic imaging
publishDate 2021
url https://hdl.handle.net/10356/146482
_version_ 1787136820829487104