Artificial neural network approaches for fluorescence lifetime imaging techniques

Wu, Gang and Nowotny, Thomas and Zhang, Yongliang and Yu, Hongqi and Li, David Day-Uei (2016) Artificial neural network approaches for fluorescence lifetime imaging techniques. Optics Letters, 41 (11). pp. 2561-2564. ISSN 0146-9592 (https://doi.org/10.1364/OL.41.002561)

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Abstract

A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. The proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, which are usually required for traditional FLIM methods. In terms of image generation, ANN-FLIM is free from iterative computations and able to generate lifetime images at least 180-fold faster than conventional least squares curve-fitting approaches. The advantages of ANN-FLIM were demonstrated on both synthesized and experimental data, showing that it has great potential to fuel current revolutions in rapid FLIM technologies.

ORCID iDs

Wu, Gang, Nowotny, Thomas, Zhang, Yongliang, Yu, Hongqi and Li, David Day-Uei ORCID logoORCID: https://orcid.org/0000-0002-6401-4263;
  • Item type:Article
    ID code:56257
    Dates:
    Date
    Event
    25 May 2016
    Published
    30 April 2016
    Accepted
    Notes:© 2016 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.
    Subjects:Science > Physics
    Department:Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
    Depositing user: Pure Administrator
    Date deposited:03 May 2016 10:31
    Last modified:11 Nov 2024 11:25
    URI:https://strathprints.strath.ac.uk/id/eprint/56257
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