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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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 |
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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 |
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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. |
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School of Chemical and Biomedical Engineering |
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School of Chemical and Biomedical Engineering Awasthi, Navchetan Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. |
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Article |
author |
Awasthi, Navchetan Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. |
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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 |
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2021 |
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https://hdl.handle.net/10356/146408 |
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