Towards unsupervised fluorescence lifetime imaging using low dimensional variable projection

Yongliang Zhang, Annie Cuyt, Wen-shin Lee, Giovanni Lo Bianco, Gang Wu, Yu Chen, David Day-Uei Li

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
211 Downloads (Pure)


Analyzing large fluorescence lifetime imaging (FLIM) data is becoming overwhelming: the latest FLIM systems easily produce massive amounts of data, making the efficient analysis more challenging than ever. In this paper we propose the combination of a custom-fit variable projection method, with a Laguerre expansion based deconvolution, to analyze bi-exponential data obtained from time-domain FLIM systems. Unlike nonlinear least squares methods, which require a suitable initial guess from an experienced researcher, the new method is free from manual interventions and hence can support automated analysis. Monte Carlo simulations are carried out on synthesized FLIM data to demonstrate the performance compared to other approaches. The performance is also illustrated on real-life FLIM data obtained from the study of autofluorescence of daisy pollen and the endocytosis of gold nanorods (GNRs) in living cells. In the latter, the fluorescence lifetimes of the GNRs are much shorter than the full width at half maximum of the instrument response function. Overall, our proposed method contains simple steps and shows great promise in realising automated FLIM analysis of large datasets.
Original languageEnglish
Pages (from-to)26777-26791
Number of pages15
JournalOptics Express
Issue number23
Early online date10 Nov 2016
Publication statusPublished - 14 Nov 2016


  • large fluorescence lifetime imaging
  • variable projection method
  • Laguerre expansion
  • deconvolution
  • Monte Carlo simulations
  • gold nanorods
  • image analysis


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