Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation.

Area of Science:

• Biophotonics
• Cellular imaging
• Microscopy techniques

Background:

• Fluorescence lifetime imaging microscopy (FLIM) offers insights into cellular metabolism, dynamics, and protein activity.
• Current FLIM methods face limitations in acquisition speed and photon detection, hindering real-time applications and wide-field imaging.
• Wide-field imaging is crucial for studying complex biological systems like cell collectives.

Purpose of the Study:

• To develop a scan-less, wide-field FLIM system with enhanced acquisition and processing speeds.
• To demonstrate the system's capability for real-time imaging of biological samples.
• To explore the potential for high-resolution, multi-megapixel FLIM imaging.

Main Methods:

• Implementation of a 0.5 MP time-gated Single Photon Avalanche Diode (SPAD) camera for scan-less, wide-field FLIM.
• Utilisation of a pre-trained artificial neural network for rapid fluorescence lifetime estimation, achieving a 1000-fold speed improvement.
• Imaging of HT1080-human fibrosarcoma cells and Convallaria samples.

Main Results:

• Achieved acquisition rates of up to 1 Hz with the developed FLIM system.
• Demonstrated significantly faster fluorescence lifetime estimation compared to traditional methods.
• Successfully imaged cellular and plant samples, showcasing the system's applicability.
• Presented a proof-of-principle 3.6 MP mosaic image, indicating scalability.

Conclusions:

• The developed scan-less wide-field FLIM system enables real-time cellular imaging.
• The integration of artificial neural networks dramatically accelerates FLIM data processing.
• This technology presents a viable pathway towards high-resolution, multi-megapixel FLIM imaging for biological research.