Tarvainen, T;
Pulkkinen, A;
Cox, BT;
Kaipio, JP;
Arridge, SR;
(2016)
Image reconstruction with noise and error modelling in quantitative photoacoustic tomography.
In: Oraevsky, AA and Wang, LV, (eds.)
Photons Plus Ultrasound: Imaging and Sensing 2016.
SPIE: San Francisco, CA, USA.
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Abstract
Quantitative photoacoustic tomography is an emerging imaging technique aimed at estimating the optical parameters inside tissue from photoacoustic images. The method proceeds from photoacoustic tomography by taking the estimated initial pressure distributions as data and estimating the absolute values of the optical parameters. Therefore, both the data and the noise of the second (optical) inverse problem are affected by the method applied to solve the first (acoustic) inverse problem. In this work, the Bayesian approach for quantitative photoacoustic tomography is taken. Modelling of noise and errors and incorporating their statistics into the solution of the inverse problem are investigated.
Type: | Proceedings paper |
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Title: | Image reconstruction with noise and error modelling in quantitative photoacoustic tomography |
Event: | SPIE BIOS |
Location: | San Francisco, CA |
Dates: | 14 February 2016 - 17 February 2016 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1117/12.2209477 |
Publisher version: | https://doi.org/10.1117/12.2209477 |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Photoacoustic tomography, quantitative imaging, inverse problems, Bayesian methods |
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 UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10113877 |
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