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

Navchetan Awasthi, Sandeep Kumar Kalva, Manojit Pramanik, Phaneendra K. Yalavarthy (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

3 Citaten (Scopus)
3 Downloads (Pure)

Samenvatting

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.

Originele taal-2Engels
Pagina's (van-tot)1320-1338
Aantal pagina's19
TijdschriftBiomedical Optics Express
Volume12
Nummer van het tijdschrift3
DOI's
StatusGepubliceerd - 1 mrt 2021

Bibliografische nota

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

Vingerafdruk

Duik in de onderzoeksthema's van 'Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data'. Samen vormen ze een unieke vingerafdruk.

Citeer dit