Fluorescence lifetime imaging microscopy (FLIM) which is capable of visualizing local molecular and physiological parameters in living cells, plays a significant role in biological sciences, chemistry, and medical research. In order to unveil dynamic cellular processes, it is necessary to develop high-speed FLIM technology. Thanks to the development of highly parallel time-to-digital convertor (TDC) arrays, especially when integrated with single-photon avalanche diodes (SPADs), the acquisition rate of high-resolution fluorescence lifetime imaging has been dramatically improved. On the other hand, these technological advances and advanced data acquisition systems have generated massive data, which significantly increases the difficulty of FLIM analysis. Traditional FLIM systems rely on time-consuming iterative algorithms to retrieve the FLIM parameters. Therefore, lifetime analysis has become a bottleneck for high-speed FLIM applications, let alone real-time or video-rate FLIM systems. Although some simple algorithms have been proposed, most of them are only able to resolve a simple FLIM decay model. On the other hand, existing FLIM systems based on CPU processing do not make use of available parallel acceleration. In order to tackle the existing problems, my study focused on introducing the state-of-art general purpose graphics processing units (GPUs) to the FLIM analysis, and building a data processing system based on both CPU and GPUs. With a large amount of parallel cores, the GPUs are able to significantly speed up lifetime analysis compared to CPU-only processing. In addition to transform the existing algorithms into GPU computing, I have developed a new high-speed and GPU friendly algorithm based on an artificial neural network (ANN). The proposed GPU-ANN-FLIM method has dramatically improved the efficiency of FLIM analysis, which is at least 1000-folder faster than some traditional algorithms, meaning that it has great potential to fuel current revolutions in high-speed high-resolution FLIM applications.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:731230 |
| Date | January 2017 |
| Creators | Wu, Gang |
| Publisher | University of Sussex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://sro.sussex.ac.uk/id/eprint/71657/ |
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