Building Fluorescence Lifetime Maps Photon-by-photon by Leveraging Spatial Correlations

Abstract

Fluorescence lifetime imaging microscopy (FLIM) has become a standard tool in the quantitative analysis of sub-cellular environments. However, quantitative FLIM analyses face several challenges. First, spatial correlations between pixels are often ignored as signal from individual pixels is analyzed independently thereby limiting spatial resolution. Second, existing methods deduce photon ratios instead of absolute lifetime maps. Next, the number of lifetime components contributing to the signal is unknown, while excited state lifetimes with <1 ns difference are difficult to discriminate. Finally, existing analyses require high photon budgets, and often cannot rigorously propagate experimental uncertainty into values over lifetime maps and number of components involved. To overcome all of these challenges simultaneously and self-consistently at once, we propose the first doubly nonparametric framework. That is, we learn the number of fluorescent species (through beta-Bernoulli process priors) and absolute lifetime maps of these species (through Gaussian process priors) by leveraging information from pulses not leading to observed photon. We benchmark our algorithm using a broad range of synthetic and experimental data and demonstrate its robustness across a number of scenarios including cases where we recover lifetime differences between components as small as 0.3 ns with merely 1000 photons.