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Efficient inclusion of total variation type priors in quantitative photoacoustic tomography

Hannukainen, A; Hyvönen, N; Majander, H; Tarvainen, T; (2016) Efficient inclusion of total variation type priors in quantitative photoacoustic tomography. SIAM Journal on Imaging Sciences , 9 (3) pp. 1132-1153. 10.1137/15M1051737. Green open access

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Abstract

Quantitative photoacoustic tomography is an emerging imaging technique aimed at estimating the distribution of optical parameters inside tissues from photoacoustic images, which are formed by combining optical information and ultrasonic propagation. This optical parameter estimation problem is ill-posed and needs to be approached within the framework of inverse problems. Photoacoustic images are three-dimensional and high-resolution. Furthermore, high-resolution reconstructions of the optical parameters are targeted. Therefore, in order to provide a practical method for quantitative photoacoustic tomography, the inversion algorithm needs to be able to perform successfully with problems of prominent size. In this work, an efficient approach for the inverse problem of quantitative photoacoustic tomography is proposed, assuming an edge-preferring prior for the optical parameters. The method is based on iteratively combining priorconditioned LSQR with a lagged diffusivity step and a linearisation of the measurement model, with the needed multiplications by Jacobians performed in a matrix-free manner. The algorithm is tested with three-dimensional numerical simulations. The results show that the approach can be used to produce accurate and high quality estimates of absorption and diffusion in complex three-dimensional geometries with moderate computation time and cost.

Type: Article
Title: Efficient inclusion of total variation type priors in quantitative photoacoustic tomography
Open access status: An open access version is available from UCL Discovery
DOI: 10.1137/15M1051737
Publisher version: http://doi.org/10.1137/15M1051737
Language: English
Additional information: © 2016, Society for Industrial and Applied Mathematics
Keywords: Quantitative photoacoustic tomography, priorconditioning, Perona–Malik, LSQR, matrix-free implementation, total variation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1497798
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