Nykanen, O;
Pulkkinen, A;
Tarvainen, T;
(2017)
Quantitative photoacoustic tomography augmented with surface light measurements.
Biomedical Optics Express
, 8
(10)
pp. 4380-4395.
10.1364/BOE.8.004380.
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Abstract
Quantitative photoacoustic tomography is an imaging modality in which distributions of optical parameters inside tissue are estimated from photoacoustic images. This optical parameter estimation is an ill-posed problem and it needs to be approached in the framework of inverse problems. In this work, utilising surface light measurements in quantitative photoacoustic tomography is studied. Estimation of absorption and scattering is formulated as a minimisation problem utilising both internal quantitative photoacoustic data and surface light data. The image reconstruction problem is studied with two-dimensional numerical simulations in various imaging situations using the diffusion approximation as the model for light propagation. The results show that quantitative photoacoustic tomography augmented with surface light data can improve both absorption and scattering estimates when compared to the conventional quantitative photoacoustic tomography.
| Type: | Article |
|---|---|
| Title: | Quantitative photoacoustic tomography augmented with surface light measurements |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/BOE.8.004380 |
| Publisher version: | http://doi.org/10.1364/BOE.8.004380 |
| Language: | English |
| Additional information: | © 2017 Optical Society of America. This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Biochemical Research Methods, Optics, Radiology, Nuclear Medicine & Medical Imaging, Biochemistry & Molecular Biology, OPTICAL-ABSORPTION COEFFICIENT, RECONSTRUCTION ALGORITHM, OPTOACOUSTIC TOMOGRAPHY, IMAGE-RECONSTRUCTION, DISTRIBUTIONS, SCATTERING, TRANSPORT, RECOVERY, BIOMEDICINE, PARAMETERS |
| 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/10043635 |
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