Statistical independence in nonlinear model-based inversion for quantitative photoacoustic tomography

The statistical independence between the distributions of different chromophores in tissue has previously been used for linear unmixing with independent component analysis (ICA). In this study, we propose exploiting this statistical property in a nonlinear model-based inversion method. The aim is to...

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Published in:Biomedical optics express 2017-11, Vol.8 (11), p.5297-5310
Main Authors: An, Lu, Saratoon, Teedah, Fonseca, Martina, Ellwood, Robert, Cox, Ben
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cited_by cdi_FETCH-LOGICAL-c384t-319fe28308d3ab6f4349038b8686f0a8c960cc657ab77080be45c550cd84785c3
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container_end_page 5310
container_issue 11
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container_title Biomedical optics express
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creator An, Lu
Saratoon, Teedah
Fonseca, Martina
Ellwood, Robert
Cox, Ben
description The statistical independence between the distributions of different chromophores in tissue has previously been used for linear unmixing with independent component analysis (ICA). In this study, we propose exploiting this statistical property in a nonlinear model-based inversion method. The aim is to reduce the sensitivity of the inversion scheme to errors in the modelling of the fluence, and hence provide more accurate quantification of the concentration of independent chromophores. A gradient-based optimisation algorithm is used to minimise the error functional, which includes a term representing the mutual information between the chromophores in addition to the standard least-squares data error. Both numerical simulations and an experimental phantom study are conducted to demonstrate that, in the presence of experimental errors in the fluence model, the proposed inversion method results in more accurate estimation of the concentrations of independent chromophores compared to the standard model-based inversion.
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title Statistical independence in nonlinear model-based inversion for quantitative photoacoustic tomography
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