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.
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Item type: Article ID code: 56257 Dates: DateEvent25 May 2016Published30 April 2016AcceptedNotes: © 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: 22 Mar 2024 02:41 URI: https://strathprints.strath.ac.uk/id/eprint/56257