GPU acceleration of time-domain fluorescence lifetime imaging

Gang Wu, Thomas Nowotny, Yu Chen, David Day-Uei Li

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Abstract

Fluorescence lifetime imaging microscopy (FLIM) plays a significant role in biological sciences, chemistry, and medical research. We propose a Graphic Processing Units (GPUs) based FLIM analysis tool suitable for high-speed and flexible time-domain FLIM applications. With a large number of parallel processors, GPUs can significantly speed up lifetime calculations compared to CPU - OpenMP (parallel computing with multiple CPU cores) based analysis. We demonstrate how to implement and optimize FLIM algorith ms on GPUs for both iterative and non-iterative FLIM analysis algorithms. The implemented algorithms have been tested on both synthesized and experimental FLIM data. The results show that at the same precision the GPU analysis can be up to 24-fold faster than its CPU - OpenMP counterpart. This means that even for high precision but time-consuming iterative FLIM algorithms, GPUs enable fast or even real-time analysis.
Original languageEnglish
Article number017001
Number of pages10
JournalJournal of Biomedical Optics
Volume21
Issue number1
Early online date6 Jan 2016
DOIs
Publication statusPublished - 6 Jan 2016

Keywords

  • time domain fluorescence lifetime
  • imaging analysis
  • FlIM
  • parallel processing
  • iterative algorithms

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    GPU-enhanced Biomedical Image Analysis

    Li, D., Nowotny, T. & Wu, G.

    1/06/1231/08/16

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