Abstract
Fluorescence microscopy can be used for evaluating distribution of medical compounds in animal tissue. Fluorescence intensity decays in time and due to scanning, but correcting for this can improve accuracy.
We present a mixed-effects model for fluorescence microscopy intensity reconstruction. Model parameters are estimated via maximum likelihood estimation, taking into account biological variability between animals and measurement uncertainty. The initial fluorescence intensity is reconstructed by decay correction.
The model is tested using different data sets. When estimating initial intensities from intensities measured on samples subjected to chemical degradation, decay rate estimates are found to be robust against variation in the ratio of measurement-induced variance to biological variance. Photobleaching rates are found to have only modest significance.
A synthetic data set consistent with previously determined parameters is generated, and a forward model is applied; the maximum likelihood estimates accurately recover the parameters, demonstrating the consistency of the model.