A Bayesian Approach for Extracting Fluorescence Lifetimes from Sparse Data Sets and Its Significance for Imaging...

Area of Science:

• Biophysics
• Spectroscopy
• Computational Chemistry

Background:

• Measuring fluorescence lifetimes in small volumes is challenging due to sparse data.
• Traditional methods like maximum likelihood (ML) assume uniform parameter distributions.
• Accurate fitting of excited-state decay functions requires robust analytical approaches.

Purpose of the Study:

• To enhance fluorescence lifetime measurements from sparse datasets.
• To implement a Bayesian framework with nonuniform probability distributions for improved accuracy.
• To investigate the utility of the Dirichlet prior distribution for rapid parameter fitting.

Main Methods:

• Applied a Bayesian framework incorporating prior knowledge and likelihood.
• Utilized the Dirichlet prior distribution for analytical solutions in parameter fitting.
• Compared Dirichlet prior performance with Gaussian and exponential priors using limited photon counts.

Main Results:

• The Dirichlet prior distribution enables rapid parameter space searching.
• Accurate experimental target lifetimes were reproduced within 20% with as few as 20 photon counts.
• Analytical solutions from the Dirichlet prior facilitate efficient parameter estimation.

Conclusions:

• Bayesian analysis with a Dirichlet prior offers a significant improvement for fluorescence lifetime measurements.
• This method is particularly effective for sparse datasets and low photon counts.
• The Dirichlet prior can rapidly provide parameters for subsequent refinement with other prior distributions.