Multispectral Tissue Classification via Autofluorescence and Reflectance
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Solution Overview
Problem
Current histopathology methods for analyzing excised tissue samples are labor-intensive, subjective, and time-consuming, and fluorescence-guided surgery is hindered by limited photostability, chemical toxicity, and poor tumor-to-background ratios, necessitating the development of a more efficient and reliable method for tissue analysis.
Innovation Solution
A method and system that utilize multispectral autofluorescence and diffuse reflectance imaging, employing a plurality of excitation lights with distinct wavelengths to produce autofluorescence emissions and diffuse reflectance signals, which are detected and processed using classifiers to determine tissue type, leveraging endogenous biomolecular signatures for diagnostic information without the need for exogenous dyes or contrast agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional histopathology methods are used, then diagnostic accuracy is maintained, but the process is labor-intensive, time-consuming, and subjective
Solution Approach 1:
The patent replaces the mechanical microscope examination system with an optical imaging system that captures multispectral images. The system uses multiple light sources (visible and UV) to illuminate tissue samples and captures images across different spectral ranges, eliminating the need for manual microscopic analysis while maintaining diagnostic capability through automated image processing and classification algorithms.
Solution Approach 2:
The patent changes the imaging parameters by capturing images at multiple wavelengths (visible and UV ranges) and processing them through different classification algorithms. The system evaluates multiple spectral parameters simultaneously to determine tissue types, replacing the single-wavelength visual inspection with multi-parameter analysis that speeds up the process while maintaining accuracy.
2Measurement precision
If fluorescence imaging agents are used for tumor detection, then diagnostic information is provided, but chemical toxicity and poor tumor-to-background ratio occur
Solution Approach 1:
The patent employs the tissue's own autofluorescence properties as the imaging agent. By exciting endogenous fluorophores within the tissue using UV and visible light, the system eliminates the need for exogenous contrast agents. The tissue itself provides the fluorescent signal for detection, removing all chemical toxicity associated with external imaging agents while maintaining the ability to detect tumor margins.
Solution Approach 2:
The patent extracts and utilizes the natural autofluorescence signal from the tissue rather than introducing external agents. By capturing and processing the inherent fluorescent emissions from tissue components (such as flavins, porphyrins, and other endogenous fluorophores), the system removes the harmful element of chemical contrast agents while preserving diagnostic information about tissue morphology and pathology.
3Measurement precision
If multiple excitation lights with distinct wavelengths are used, then comprehensive tissue analysis is achieved, but device complexity increases
Solution Approach 1:
The patent merges multiple imaging capabilities into a single integrated system. By combining visible light and UV light illumination paths, along with their respective image capture and processing systems, into one unified platform, the patent achieves comprehensive tissue analysis without requiring separate equipment for each imaging mode. The merged system processes multiple spectral data streams simultaneously to improve classification accuracy.
Solution Approach 2:
The patent creates a universal imaging system that can capture images across multiple spectral ranges (visible and UV) using a single device architecture. The system is designed to accommodate multiple light sources and detectors that work together to provide comprehensive tissue characterization, making the device multi-functional for various tissue types and diagnostic applications without requiring separate specialized equipment for each function.
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
This approach provides rapid, label-free, and cost-effective tissue analysis, offering improved diagnostic accuracy and specificity by combining autofluorescence and reflectance imaging to identify biomolecules and tissue morphologies, potentially reducing false positives and enhancing surgical and pathological assessments.
Implementation Method 1
at least one of the excitation light centered wavelengths is configured to produce autofluorescence (AF) emissions from one or more biomolecules associated with the tissue sample
Implementation Method 2
at least one of the excitation light centered wavelengths is configured to produce diffuse reflectance signals from the tissue sample
Data Source
AI summary
A method and system of analyzing an ex-vivo tissue sample is provided. The method includes interrogating the tissue sample a plurality of times, each interrogation using at least one excitation light centered on a wavelength distinct from the others, at least one excitation light produces AF emissions from one or more biomolecules associated with the tissue sample, and another is produces diffuse reflectance signals from the tissue sample; b) using a photodetector to detect the AF emissions or diffuse reflectance signals from the tissue sample, producing photodetector signals representative thereof; c) processing the photodetector signals attributable to the AF emissions using a first trained classifier to determine first data sets indicative of biomolecules; d) processing the photodetector signals attributable to the diffuse reflectance signals using a second trained classifier to determine one or more second data sets; and e) determining a type of the tissue sample.