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Proceedings Paper

Exploiting statistical independence for quantitative photoacoustic tomography
Author(s): Lu An; Teedah Saratoon; Martina Fonseca; Robert Ellwood; Ben Cox
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Paper Abstract

To unlock the full capability of photoacoustic tomography as a quantitative, high resolution, molecular imaging modality, the problem of quantitative photoacoustic tomography must be solved. The aim in this is to extract clinically relevant functional information from photoacoustic images by finding the concentrations of the chromophores in the tissue. This is a challenging task due to the effect of the unknown but spatially and spectrally varying light fluence within the tissue. Many inversion schemes that include a model of the fluence have been proposed, but these have yet to make an impact in pre-clinical or clinical imaging. In this study, the statistical independence of the chromophore's distributions is proposed as a means of improving the robustness and hence the usefulness of the model-based inversion methods. This was achieved by minimising the mutual information between the estimated chromophore distributions in addition to the least squares data error within a gradient-based optimisation scheme. By applying the proposed inversion scheme to simulated multiwavelength photoacoustic images, it was shown that more accurate estimates for the concentrations of independent chromophores could be obtained in the presence of errors in the model parameters.

Paper Details

Date Published: 23 March 2017
PDF: 7 pages
Proc. SPIE 10064, Photons Plus Ultrasound: Imaging and Sensing 2017, 1006419 (23 March 2017); doi: 10.1117/12.2250290
Show Author Affiliations
Lu An, Univ. College London (United Kingdom)
Teedah Saratoon, Univ. College London (United Kingdom)
Martina Fonseca, Univ. College London (United Kingdom)
Robert Ellwood, Univ. College London (United Kingdom)
Ben Cox, Univ. College London (United Kingdom)

Published in SPIE Proceedings Vol. 10064:
Photons Plus Ultrasound: Imaging and Sensing 2017
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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