FLIM Phasor Segmentation for Automated Lifetime Unmixing
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Solution Overview
Problem
Conventional methods for lifetime-based unmixing in fluorescence microscopy require extensive a priori knowledge and are not easily reproducible or automatable, especially when dealing with complex samples containing multiple fluorophores with multi-exponential behavior.
Innovation Solution
A processor that automates the detection of fluorescence lifetime classes by generating a phasor plot, partitioning the image into segments, and performing lifetime classification based on total photon counts and average arrival times, without requiring prior knowledge about the sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lifetime-based unmixing methods are used, then fluorophore identification can be achieved, but extensive a priori knowledge is required and the process is not easily automatable
Solution Approach 1:
The system performs self-service by automatically selecting representative pixels and determining lifetime classes without requiring user intervention or prior knowledge about the sample. The algorithm independently identifies fluorophores by analyzing photon arrival time distributions and clustering pixels with similar lifetime characteristics.
Solution Approach 2:
The invention changes the approach from using fixed a priori knowledge to dynamically determining lifetime parameters from the data itself. By varying the analysis from conventional fitting approaches to phasor-based clustering, the system adapts to different sample compositions without requiring pre-specified parameters.
2Measurement precision
If fitting approaches are used to determine average fluorescence lifetime, then results can be obtained for simple cases with two fluorophores, but the method becomes ineffective when more than two fluorophores are present or when multi-exponential behavior occurs
Solution Approach 1:
The invention segments the complex lifetime distribution into multiple distinct lifetime classes by clustering pixels with similar photon arrival time characteristics. This segmentation allows the system to handle multiple fluorophores and multi-exponential behavior by treating each cluster as a representative of a specific lifetime component.
Solution Approach 2:
The system transitions from conventional one-dimensional fitting approaches to a two-dimensional phasor space representation. By mapping lifetime data onto real and imaginary components, the method gains an additional dimension for discrimination, enabling effective separation of multiple fluorophores and complex decay behaviors that cannot be resolved by traditional fitting.
3Loss of information
If a conventional phasor approach is used to visualize overall contributions, then all species can be seen in the phasor plot, but users must empirically search all positions to find the lifetime position corresponding to the structure of interest
Solution Approach 1:
The invention extracts specific lifetime class information from the complete phasor plot by automatically identifying and isolating representative pixels for each lifetime class. This extraction process separates the mixed signal into distinct components, allowing users to directly access specific fluorophore information without manually searching through the entire phasor space.
4Measurement precision
If spectral unmixing is used to account for spectral overlap, then quantitative analysis can be achieved, but the method does not utilize lifetime information to resolve overlapping signals
Solution Approach 1:
The invention merges spectral information with lifetime information by integrating phasor analysis into the unmixing process. This combination allows the system to simultaneously utilize both spectral characteristics and temporal decay patterns, providing enhanced discrimination of fluorophores with overlapping spectra but distinct lifetime signatures.
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
Enables reliable and straightforward lifetime-based unmixing by detecting fluorophore classes in an automated manner, improving the precision and reproducibility of fluorescence microscopy.
Implementation Method 1
Time-correlated single-photon counting (TCSPC) is usually employed to record a fluorescence decay histogram providing information on both photon count and photon arrival time for each pixel
Implementation Method 2
Fluorescence-lifetime imaging microscopy (FLIM) is a specific imaging technique which can be used to identify a fluorophore in a sample by determining a decay rate of photons emitted by the fluorophore
Implementation Method 3
A phasor transform that is applied to a histogram representing photon counts as a function of arrival times yields two quantities which are mapped to a two-dimensional space called phasor space
Data Source
Figure 1~2
Figure 3
Figure 4a~4c
AI summary
A processor (106) for lifetime-based unmixing in fluorescence microscopy is configured to acquire an image (310a) having a plurality of pixels, each pixel providing information on both photon count and photon arrival times. The processor (106) is configured to generate a phasor plot (310b) which is a vector space representation of the image (310a). The processor (106) is configured to partition the image (310a) into multiple image segments (414a), each image segment (414a) including a subset from the plurality of pixels. The processor (106) is configured to evaluate the image segments (414a) according to total photon counts of the corresponding subsets of pixels. The processor (106) is configured to execute a lifetime classification comprising a step of selecting from the image segments (414a) an image segment evaluated with the largest total photon count; a step of determining a region of interest (ROI1) in the image (310a) encompassing the image segment (414a), a step of determining a phasor subset (PP1, PP2, PP3) in the phasor plot (310b) corresponding to the region of interest (ROI1); and a step of generating a lifetime class including those image segments consistent with the phasor subset (PP1, PP2,PP3). The processor (106) is configured to generate a plurality of disjunct lifetime classes by iteratively executing the lifetime classification based on the remaining image segments not assigned to one of the preceding lifetime classes and to perform lifetime-based unmixing using the disjunct life-time classes.