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We present novel Bayesian methods for the analysis of exponential decay data that exploit the evidence carried by every detected decay event and enables robust extension to advanced processing. Our algorithms are presented in the context of fluorescence lifetime imaging microscopy (FLIM) and particular attention has been paid to model the time-domain system (based on time-correlated single photon counting) with unprecedented accuracy. We present estimates of decay parameters for mono- and bi-exponential systems, offering up to a factor of two improvement in accuracy compared to previous popular techniques. Results of the analysis of synthetic and experimental data are presented, and areas where the superior precision of our techniques can be exploited in Förster Resonance Energy Transfer (FRET) experiments are described. Furthermore, we demonstrate two advanced processing methods: decay model selection to choose between differing models such as mono- and bi-exponential, and the simultaneous estimation of instrument and decay parameters.

Original publication

DOI

10.1371/journal.pone.0158404

Type

Journal article

Journal

PLoS One

Publication Date

2016

Volume

11

Keywords

Algorithms, Bayes Theorem, Cell Line, Tumor, Fluorescence, Fluorescence Resonance Energy Transfer, Humans, Least-Squares Analysis, Likelihood Functions, Microscopy, Fluorescence, Models, Theoretical, Neoplasms