Deep Learning SR-FLIM for Diffraction-Limited Resolution

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Solution Overview

Problem

Conventional FLIM techniques are limited by spatial resolution due to light diffraction, hindering high-resolution studies of intricate intracellular structures and dynamic processes, and existing SR imaging methods face challenges such as high laser power, slow imaging speed, and high costs.

Innovation Solution

A deep learning-based SR-FLIM method that enhances spatial resolution within a conventional confocal FLIM system by using paired fluorescence images and a trained deep learning model to generate high-quality SR fluorescence intensity and lifetime images.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional FLIM techniques are used, then fluorescence lifetime measurement is achieved, but spatial resolution is limited by light diffraction

Engineering Contradiction:

Improvespatial resolutionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

A deep learning model serves as an intermediary between conventional FLIM images and super-resolution images. The model takes standard FLIM images as input and generates super-resolution images with enhanced spatial resolution, effectively mediating the transition without requiring complex hardware modifications

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces complex mechanical/optical super-resolution systems (like STED or SIM microscopes) with a computational approach using deep learning. This substitution maintains measurement precision while significantly reducing system complexity and cost

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If STED or SIM super-resolution techniques are used, then spatial resolution is enhanced, but laser power requirements and system cost increase

Engineering Contradiction:

Improvespatial resolutionVSAvoidlaser power

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent uses standard confocal FLIM systems (cheaper, widely available equipment) instead of expensive STED or SIM systems. The computational deep learning model processes the images from these accessible systems to achieve super-resolution, making the technology more accessible and cost-effective

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

Solution Approach 2:

The patent substitutes high-power laser-based optical systems with a computational deep learning approach. This replacement dramatically reduces energy consumption while achieving comparable or superior spatial resolution through intelligent image processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If STED or SIM super-resolution techniques are used, then spatial resolution is enhanced, but imaging speed decreases

Engineering Contradiction:

Improvespatial resolutionVSAvoidimaging speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The deep learning model is pre-trained on large datasets of image pairs (standard resolution and super-resolution). This preliminary training enables the model to rapidly generate super-resolution images during actual use without requiring time-consuming iterative optimization during imaging

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces slow, mechanically complex super-resolution imaging systems with a fast computational deep learning model. This substitution maintains high spatial resolution while significantly improving imaging speed by eliminating mechanical scanning and iterative optimization steps

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The method significantly improves spatial resolution of FLIM images, enabling detailed exploration of biological and chemical structures, while maintaining system simplicity and cost-effectiveness.

Implementation Method 1

A deep learning-based SR-FLIM method includes the steps of: inputting a training set into a network for training; inputting FLIM data into the trained network to obtain SR fluorescence intensity information

Methodology Applied

Scientific EffectDeep learning:

Implementation Method 2

By precisely measuring the average time required for fluorescent molecules to transition from an excited state to a ground state, known as a fluorescence lifetime

Methodology Applied

Scientific EffectFluorescence: Fluorescence

Data Source

PatentUS20260044932A1Deep learning-based super-resolution fluorescence lifetime imaging microscopy method

Publication Date: 2026.02.12 SHENZHEN UNIV

AI summary

A deep learning-based super-resolution fluorescence lifetime imaging microscopy (SR-FLIM) method includes the steps of: S1, performing fluorescence microscopic imaging on a sample to obtain confocal intensity images and stimulated emission depletion (STED) intensity images at a same location; S2, co-registering the acquired confocal and STED intensity images; S3, pairing the co-registered confocal and STED intensity images as input (Input) and ground truth (GT) to assemble a dataset; S4, partitioning the dataset into training and validation sets following a predefined ratio; and S5, constructing a network, and selecting hyperparameters and an optimizer. This method may achieve SR-FLIM within a conventional confocal FLIM system, surpassing spatial resolution limitations of FLIM, breaking through resolution barriers of conventional optical microscopy, while preserving normal fluorescence lifetime characteristics of fluorescent probes.