Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146408
Title: Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
Authors: Awasthi, Navchetan
Kalva, Sandeep Kumar
Pramanik, Manojit
Yalavarthy, Phaneendra K.
Keywords: Engineering::Bioengineering
Issue Date: 2021
Source: 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
Journal: Biomedical Optics Express
Abstract: 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.
URI: https://hdl.handle.net/10356/146408
ISSN: 2156-7085
DOI: 10.1364/BOE.415182
Rights: © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

Page view(s)

39
Updated on Jun 25, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.