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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.
- time domain fluorescence lifetime
- imaging analysis
- parallel processing
- iterative algorithms
Wu, G., Nowotny, T., Chen, Y., & Li, D. D-U. (2016). GPU acceleration of time-domain fluorescence lifetime imaging. Journal of Biomedical Optics, 21(1), . https://doi.org/10.1117/1.JBO.21.1.017001