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Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
Published on: January 18, 2017
Hardware implementation algorithm and error analysis of high-speed fluorescence lifetime sensing systems using
Day-Uei Li1, Bruce Rae, Robin Andrews
1University of Edinburgh, Institute for Integrated Micro and Nano Systems (IMNS), School of Engineering, Edinburgh, Scotland. David.Li@ed.ac.uk
Journal of Biomedical Optics
|March 10, 2010
Summary
A novel hardware algorithm using the center-of-mass method (CMM) enables rapid fluorescence lifetime calculations. This method offers faster analysis for real-time applications like clinical diagnosis compared to traditional software approaches.
Area of Science:
- Optics and Photonics
- Biomedical Imaging
- Computational Science
Background:
- Fluorescence lifetime imaging microscopy (FLIM) is crucial for various applications, including clinical diagnosis.
- Current FLIM analysis software often relies on iterative methods (e.g., least-squares, maximum likelihood estimation), which can be time-consuming.
- There is a need for faster, hardware-based solutions for real-time FLIM data processing.
Purpose of the Study:
- To introduce a new, simple, high-speed, hardware-only fluorescence lifetime sensing algorithm based on the center-of-mass method (CMM).
- To deduce the signal-to-noise ratio of the CMM algorithm using statistical theory.
- To propose a real-time hardware implementation of the CMM FLIM algorithm for CMOS imaging technology.
Main Methods:
- Developed a hardware-only integration-based algorithm utilizing the center-of-mass method (CMM) for fluorescence lifetime calculations.
- Deduced the signal-to-noise ratio (SNR) of the CMM algorithm based on statistical theory.
- Proposed a real-time hardware implementation on a field-programmable gate array (FPGA) for single-photon avalanche diode arrays in CMOS imaging technology.
Main Results:
- The CMM algorithm provides direct calculation of fluorescence lifetime using photon counts and timing information.
- The proposed hardware implementation offers significantly faster analysis compared to conventional software-based methods.
- Performance was validated using standard fluorescent samples: Fluorescein, Coumarin 6, and 1,8-anilinonaphthalenesulfonate.
Conclusions:
- The CMM-based hardware algorithm presents a simple, high-speed alternative for fluorescence lifetime sensing.
- This approach facilitates real-time FLIM analysis, enhancing potential for applications such as clinical diagnosis.
- The developed hardware implementation demonstrates feasibility and efficiency for advanced imaging systems.
