Paper
14 June 2011 Uncertainty analysis for fluorescence tomography with Monte Carlo method
Alice Reinbacher-Köstinger, Manuel Freiberger, Hermann Scharfetter
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Abstract
Fluorescence tomography seeks to image an inaccessible fluorophore distribution inside an object like a small animal by injecting light at the boundary and measuring the light emitted by the fluorophore. Optical parameters (e.g. the conversion efficiency or the fluorescence life-time) of certain fluorophores depend on physiologically interesting quantities like the pH value or the oxygen concentration in the tissue, which allows functional rather than just anatomical imaging. To reconstruct the concentration and the life-time from the boundary measurements, a nonlinear inverse problem has to be solved. It is, however, difficult to estimate the uncertainty of the reconstructed parameters in case of iterative algorithms and a large number of degrees of freedom. Uncertainties in fluorescence tomography applications arise from model inaccuracies, discretization errors, data noise and a priori errors. Thus, a Markov chain Monte Carlo method (MCMC) was used to consider all these uncertainty factors exploiting Bayesian formulation of conditional probabilities. A 2-D simulation experiment was carried out for a circular object with two inclusions. Both inclusions had a 2-D Gaussian distribution of the concentration and constant life-time inside of a representative area of the inclusion. Forward calculations were done with the diffusion approximation of Boltzmann's transport equation. The reconstruction results show that the percent estimation error of the lifetime parameter is by a factor of approximately 10 lower than that of the concentration. This finding suggests that lifetime imaging may provide more accurate information than concentration imaging only. The results must be interpreted with caution, however, because the chosen simulation setup represents a special case and a more detailed analysis remains to be done in future to clarify if the findings can be generalized.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alice Reinbacher-Köstinger, Manuel Freiberger, and Hermann Scharfetter "Uncertainty analysis for fluorescence tomography with Monte Carlo method", Proc. SPIE 8088, Diffuse Optical Imaging III, 80881P (14 June 2011); https://doi.org/10.1117/12.889454
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KEYWORDS
Monte Carlo methods

Fluorescence tomography

Error analysis

Luminescence

Tissue optics

Uncertainty analysis

Data modeling

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