20240035971. SYSTEM AND METHOD FOR FLUORESCENCE LIFETIME IMAGING simplified abstract (Board of Regents, The University of Texas System)

SYSTEM AND METHOD FOR FLUORESCENCE LIFETIME IMAGING

Organization Name

Board of Regents, The University of Texas System

Inventor(s)

Hsin-Chih Yeh of Austin TX (US)

Yuan-I Chen of Austin TX (US)

Yin-Jui Chang of Austin TX (US)

Shih-Chu Liao of Champaign IL (US)

Trung Duc Nguyen of Austin TX (US)

Soonwoo Hong of Austin TX (US)

Yu-An Kuo of Austin TX (US)

Hsin-Chin Li of Austin TX (US)

SYSTEM AND METHOD FOR FLUORESCENCE LIFETIME IMAGING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240035971 titled 'SYSTEM AND METHOD FOR FLUORESCENCE LIFETIME IMAGING

Simplified Explanation

The abstract describes a fluorescence lifetime imaging microscopy system that uses a trained neural network to calculate fluorescence decay parameters from measured emissions of energy. The system includes a microscope with an excitation source and a detector, as well as a computer-readable medium with instructions for data collection and analysis.

• The system collects measured emissions of energy from an imaging target and provides the data to a trained neural network.
• The neural network calculates at least one fluorescence lifetime parameter from the measured data, using a generative adversarial network for training.
• The measured data includes an input fluorescence decay histogram.

Potential applications of this technology:

• Biomedical research: This system can be used in biological and medical research to study the fluorescence lifetime of various biomolecules, such as proteins and DNA, providing insights into their structure and dynamics.
• Drug discovery: The system can aid in the development of new drugs by analyzing the fluorescence lifetime of fluorescently labeled compounds, helping researchers understand their behavior and interactions within cells.
• Materials science: The technology can be applied in materials science to investigate the fluorescence properties of materials, such as polymers and nanoparticles, enabling the characterization of their composition and structure.

Problems solved by this technology:

• Improved imaging accuracy: By using a trained neural network, the system can accurately calculate fluorescence lifetime parameters, providing more precise and reliable imaging results compared to traditional methods.
• Time-efficient analysis: The automated analysis performed by the neural network reduces the time and effort required for manual data interpretation, allowing for faster data processing and analysis.
• Enhanced data interpretation: The system's ability to calculate fluorescence decay parameters helps researchers gain a deeper understanding of the underlying molecular processes and dynamics, leading to new insights and discoveries.

Benefits of this technology:

• Advanced imaging capabilities: The system enables the measurement and analysis of fluorescence lifetime parameters, providing additional information beyond traditional intensity-based imaging techniques.
• Increased efficiency: The automated analysis performed by the trained neural network speeds up data processing and analysis, allowing researchers to focus on interpreting the results and drawing meaningful conclusions.
• Versatile applications: The technology can be applied in various fields, including biology, medicine, and materials science, offering a wide range of potential applications and research opportunities.

Original Abstract Submitted

a fluorescence lifetime imaging microscopy system comprises a microscope comprising an excitation source configured to direct an excitation energy to an imaging target, and a detector configured to measure emissions of energy from the imaging target, and a non-transitory computer-readable medium with instructions stored thereon, which perform steps comprising collecting a quantity of measured emissions of energy from the imaging target as measured data, providing a trained neural network configured to calculate fluorescent decay parameters from the quantity of measured emissions of energy, providing the data to the trained neural network, and calculating at least one fluorescence lifetime parameter with the neural network from the measured data, wherein the measured data comprises an input fluorescence decay histogram, and wherein the neural network was trained by a generative adversarial network. a method of training a neural network and a method of acquiring an image are also described.