Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105336
Title: Fractional regularization to improve photoacoustic tomographic image reconstruction
Authors: Prakash, Jaya
Sanny, Dween
Kalva, Sandeep Kumar
Pramanik, Manojit
Yalavarthy, Phaneendra K.
Keywords: Photoacoustic Tomography
Image Reconstruction
Engineering::Chemical engineering
Issue Date: 2019
Source: Prakash, J., Sanny, D., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2019). Fractional regularization to improve photoacoustic tomographic image reconstruction. IEEE Transactions on Medical Imaging, 38(8), 1935-1947. doi:10.1109/TMI.2018.2889314
Series/Report no.: IEEE Transactions on Medical Imaging
Abstract: Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include, Tikhonov, `1-norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This work introduces a fractional framework, to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, `1-norm, and total-variation regularization schemes. Moreover, an automated method for choosing the fractional power was also proposed. It was shown theoretically and with numerical experiments that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework outperforms the standard regularization schemes, Tikhonov, `1-norm, and total-variation by 54% in numerical simulations, experimental phantoms and in vivo rat data in terms of observed contrast/signal-to-noise-ratio of the reconstructed images.
URI: https://hdl.handle.net/10356/105336
http://hdl.handle.net/10220/49543
ISSN: 0278-0062
DOI: 10.1109/TMI.2018.2889314
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TMI.2018.2889314
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

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