Histogram clustering for rapid time-domain fluorescence lifetime image analysis

Fluorescence lifetime imaging (FLIM) [1] is a crucial technique for assessing microenvironments of fluorophores, such as pH, Ca2+, O2, viscosity, or temperature [2-5]. Combining with Forster Resonance Energy Transfer (FRET) techniques [6], FLIM can be a powerful "quantum ruler" to measure protein conformations and interactions [7]. In contrast to fluorescence intensity imaging, FLIM is independent of fluorescence intensities and fluorophore concentrations, making FLIM a robust quantitative imaging technique for life sciences applications [8,9], medical diagnosis [10], drug developments [11,12], and flow diagnosis [13-15]. A fluorescence decay is usually modeled as a sum of exponential decay functions: We propose a histogram clustering (HC) method to accelerate fluorescence lifetime imaging (FLIM) analysis in pixel-wise and global fitting modes. The proposed method's principle was demonstrated, and the combinations of HC with traditional FLIM analysis were explained. We assessed HC methods with both simulated and experimental datasets. The results reveal that HC not only increases analysis speed (up to 106 times) but also enhances lifetime estimation accuracy. Fast lifetime analysis strategies were suggested with execution times around or below 30 mu s per histograms on MATLAB R2016a, 64-bit with the Intel Celeron CPU (2950M @ 2GHz).