Open AccessDimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
Author(s) -
Navchetan Awasthi,
Sandeep Kumar Kalva,
Manojit Pramanik,
Phaneendra K. Yalavarthy
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.415182
Subject(s) - singular value decomposition , tikhonov regularization , inverse problem , lanczos resampling , prior probability , iterative reconstruction , deconvolution , computer science , robustness (evolution) , algorithm , regularization (linguistics) , tomographic reconstruction , singular value , synthetic data , curse of dimensionality , mathematics , artificial intelligence , bayesian probability , physics , mathematical analysis , biochemistry , eigenvalues and eigenvectors , chemistry , quantum mechanics , gene
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.