Tissue Section Classification via Optical-Mass Spec Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current histologic classification methods face limitations in spatial resolution and accuracy due to the disparity between optical and mass spectrometric images, with mass spectrometric images having a resolution significantly worse than optical images, leading to suboptimal correlation and classification quality.
Innovation Solution
Acquiring both high-resolution light-optical and mass spectrometric images of the same tissue section and combining their information for classification, where the optical image's higher spatial resolution enhances the accuracy of histologic classification by correlating morphologic and molecular data from subareas, including individual cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mass spectrometric imaging is used for molecular information acquisition, then molecular substance detection capability is improved, but spatial resolution deteriorates significantly compared to optical imaging
Solution Approach 1:
The patent merges mass spectrometric imaging data with high-resolution optical histology images by establishing spatial correspondence between the two datasets. Mass spectrometric data providing molecular information is overlaid with optical images providing high-resolution structural information, creating a unified diagnostic view that combines both molecular and morphological characteristics without compromising the spatial resolution of the optical component.
2Measurement precision
If high-resolution optical imaging is used for structural analysis, then spatial resolution is improved, but molecular information content deteriorates due to lack of molecular specificity
Solution Approach 1:
The patent combines optical histology images with mass spectrometric imaging data to create a composite diagnostic system. The high-resolution optical images maintain their structural detail while being enriched with molecular information from mass spectrometry through spatial correlation and data overlay, allowing simultaneous visualization of both morphological and molecular characteristics.
3Ease of manufacture
If separate analysis of optical and mass spectrometric images is performed, then each modality can be optimized independently, but classification quality deteriorates due to lack of integrated information
Solution Approach 1:
The patent implements an integrated classification system that combines features from both optical histology images and mass spectrometric imaging data. The system processes both modalities independently to maintain their respective optimizations, then merges their feature sets through machine learning algorithms that consider both morphological and molecular characteristics simultaneously, achieving superior diagnostic accuracy compared to either modality alone.
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 significantly improves the quality of histologic classification by integrating high-resolution optical and mass spectrometric information, allowing for precise tissue differentiation and disease status determination, overcoming the limitations of previous methods by maintaining undiminished spatial resolution and information integrity.
Implementation Method 1
The solvent then evaporates and the matrix substance crystallizes; the biological substances in the matrix crystals crystallize at the same time
Implementation Method 2
Bombarding a homogenized sample thus prepared with short laser pulses of sufficient energy causes the matrix substance to explosively vaporize and the biological substances to be ionized
Implementation Method 3
Bombarding a homogenized sample thus prepared with short laser pulses of sufficient energy causes the matrix substance to explosively vaporize
Data Source
Figure 1A~1E
Figure 2F~2G
AI summary
The present invention relates to a method for the histologic classification of a tissue section. The invention consists in acquiring a mass spectrometric image and a light-optical image of the same tissue section (the optical image having a higher spatial resolution than the mass spectrometric image) and combining optical information on the structures of a subarea of the tissue section with mass spectrometric information on the subarea (the structures not being spatially resolved in the mass spectrometric image).