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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Towards unsupervised fluorescence lifetime imaging using low dimensional variable projection
Author(s): Zhang, Yongliang
Cuyt, Annie
Lee, Wen-shin
Lo Bianco, Giovanni
Wu, Gang
Chen, Yu
Li, David Day-Uei
Keywords: Photon counting
Image analysis
Lifetime-based sensing
Time-resolved imaging.
Issue Date: 14-Nov-2016
Citation: Zhang Y, Cuyt A, Lee W, Lo Bianco G, Wu G, Chen Y & Li DD (2016) Towards unsupervised fluorescence lifetime imaging using low dimensional variable projection. Optics Express, 24 (23), pp. 26777-26791.
Abstract: Analyzing large fluorescence lifetime imaging (FLIM) data is becoming overwhelming; the latest FLIM systems easily produce massive amounts of data, making an 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 data sets.
DOI Link: 10.1364/oe.24.026777
Rights: Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
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