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Low-rate smartphone videoscopy for microsecond luminescence lifetime imaging with machine learning
Yan Wang1, Sina Sadeghi1, Alireza Velayati1
1Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
PNAS Nexus
|October 13, 2023
Summary
We developed a low-cost smartphone system for luminescence lifetime imaging using a virtual chopper. This method eliminates expensive equipment, enabling wider applications for time-resolved imaging.
Area of Science:
- Optics and Photonics
- Biomedical Imaging
- Materials Science
Background:
- Traditional time-resolved imaging systems are costly, requiring specialized equipment like high-speed detectors and excitation sources.
- Existing methods often involve complex mechanical choppers, limiting accessibility and portability.
- There is a need for cost-effective and miniaturized solutions for luminescence lifetime imaging.
Purpose of the Study:
- To present a novel, cost-effective, and miniaturized smartphone-based system for 2D luminescence lifetime imaging.
- To demonstrate the feasibility of a videoscopy-based virtual chopper (V-chopper) mechanism for time-resolved measurements.
- To integrate machine learning for automated image analysis and lifetime determination.
Main Methods:
- Developed a smartphone lifetime imaging system integrated with a pulsed ultraviolet (UV) light-emitting diode (LED).
- Employed a videoscopy-based virtual chopper (V-chopper) mechanism to generate time-delayed images without excitation synchronization.
- Utilized a convolutional neural network (CNN) for accurate classification of gated images and lifetime decay analysis.
- Validated the system using Europium (Eu) complex dyes with varying luminescence lifetimes.
Main Results:
- The V-chopper system successfully performed 2D luminescence lifetime imaging at a low frame rate (30 fps).
- A CNN model achieved >99.5% accuracy in distinguishing gated images across different decay cycles.
- The system demonstrated a detection limit of approximately 75 µs, with potential for shorter lifetimes.
- The smartphone-based system effectively replaced expensive and complex traditional time-resolved equipment.
Conclusions:
- The smartphone V-chopper system offers a cost-effective and miniaturized alternative for luminescence lifetime imaging.
- This technology significantly reduces the barrier to entry for time-resolved imaging applications.
- The V-chopper methodology holds promise for expanding the accessibility and application range of lifetime imaging techniques.
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