AI Article Synopsis
- The study introduces a nonlinear model-based method for quantifying the concentration of different chromophores in tissue, building on the idea of their statistical independence.
- It aims to improve accuracy by reducing the method's sensitivity to modeling errors related to light fluence.
- Numerical simulations and a phantom experiment show that this new approach yields better concentration estimates than traditional model-based methods, even when there are errors in the fluence model.
Article Abstract
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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695971 | PMC |
| http://dx.doi.org/10.1364/BOE.8.005297 | DOI Listing |
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