Artificial neural network approaches for fluorescence lifetime imaging techniques

Wu, Gang, Nowotny, Thomas, Zhang, Yongliang, Yu, Hong-Qi 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

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Abstract

A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. In terms of image generation, the proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, and it can generate lifetime images at least 180-fold faster than conventional least squares curve-fitting software tools. 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.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Subjects: Q Science > QD Chemistry > QD0071 Analytical chemistry
T Technology > T Technology (General) > T0174.7 Nanotechnology
T Technology > TA Engineering (General). Civil engineering (General) > TA0164 Bioengineering
Depositing User: Gang Wu
Date Deposited: 20 Jun 2016 12:28
Last Modified: 07 Mar 2017 04:24
URI: http://sro.sussex.ac.uk/id/eprint/61611

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