Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data

The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (S...

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Main Authors: Awasthi, Navchetan, Kalva, Sandeep Kumar, Pramanik, Manojit, Yalavarthy, Phaneendra K.
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146408
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1464082023-12-29T06:48:50Z Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data Awasthi, Navchetan Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. School of Chemical and Biomedical Engineering Engineering::Bioengineering Photoacoustic Tomography (PAT) Singular Value Decomposition (SVD) The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data. The method was shown to be superior as compared to total variation regularization, basis pursuit deconvolution and Lanczos Tikhonov based regularization and provided improved performance in case of noisy data. The numerical and experimental cases show that the improvement can be as high as 8.1 dB in signal to noise ratio of the reconstructed image and 67.98% in root mean square error in comparison to the state of the art methods. Published version Department of Science and Technology, Ministry of Science and Technology, India (DST-ICPS (T-851)). PKY acknowledges the DST-ICPS cluster funding (T-851) for the data science program. 2021-02-16T05:54:52Z 2021-02-16T05:54:52Z 2021 Journal Article Awasthi, N., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2021). Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data. 12(3), 1320-1338. doi:10.1364/BOE.415182 2156-7085 https://hdl.handle.net/10356/146408 10.1364/BOE.415182 3 12 1320 1338 en Biomedical Optics Express © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. 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 Tomography (PAT)
Singular Value Decomposition (SVD)
spellingShingle Engineering::Bioengineering
Photoacoustic Tomography (PAT)
Singular Value Decomposition (SVD)
Awasthi, Navchetan
Kalva, Sandeep Kumar
Pramanik, Manojit
Yalavarthy, Phaneendra K.
Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
description The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data. The method was shown to be superior as compared to total variation regularization, basis pursuit deconvolution and Lanczos Tikhonov based regularization and provided improved performance in case of noisy data. The numerical and experimental cases show that the improvement can be as high as 8.1 dB in signal to noise ratio of the reconstructed image and 67.98% in root mean square error in comparison to the state of the art methods.
author2 School of Chemical and Biomedical Engineering
author_facet School of Chemical and Biomedical Engineering
Awasthi, Navchetan
Kalva, Sandeep Kumar
Pramanik, Manojit
Yalavarthy, Phaneendra K.
format Article
author Awasthi, Navchetan
Kalva, Sandeep Kumar
Pramanik, Manojit
Yalavarthy, Phaneendra K.
author_sort Awasthi, Navchetan
title Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
title_short Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
title_full Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
title_fullStr Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
title_full_unstemmed Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
title_sort dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
publishDate 2021
url https://hdl.handle.net/10356/146408
_version_ 1787136579451486208