Endoscope Oxygen Saturation Imaging via Dynamic Tissue Correction
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Solution Overview
Problem
Existing endoscope systems face challenges in accurately calculating oxygen saturation when the range of an organ in the region of interest includes multiple tissues, due to variations in angle of view and tissue type, leading to inconsistent correction values and deviations in calculated oxygen saturation.
Innovation Solution
An endoscope system that acquires image signals from specific wavelength ranges sensitive to blood hemoglobin and a specific pigment, performs multiple correction value calculations, sets a representative value from these calculations, and uses this value to accurately calculate oxygen saturation, while also canceling previous correction values and highlighting regions with low oxygen saturation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If correction imaging is performed using a fixed region of interest, then correction value calculation is simplified, but the correction value becomes inconsistent when angle of view or tissue type changes
Solution Approach 1:
The patent applies dynamics by making the region of interest adjustable rather than fixed. The processor dynamically changes the region of interest based on detected tissue type and angle of view variations, allowing the correction value calculation to adapt to different imaging conditions while maintaining consistency and accuracy.
Solution Approach 2:
The patent implements feedback by detecting tissue type and angle of view changes, then using this information to adjust the region of interest for correction imaging. This closed-loop approach ensures that the correction value remains consistent and accurate despite variations in imaging conditions.
2Productivity
If a single correction value is calculated from one correction image, then processing time is reduced, but oxygen saturation calculation accuracy decreases when multiple tissues are present
Solution Approach 1:
The patent applies segmentation by dividing the correction value calculation into multiple independent calculations, each corresponding to a different tissue type detected in the region of interest. The processor calculates separate correction values for each tissue type and then combines them to obtain the final oxygen saturation, maintaining accuracy while managing processing complexity.
Solution Approach 2:
The patent implements local quality by calculating different correction values for different tissue types within the same region of interest. Each tissue type receives its specific correction treatment based on its optical properties, allowing accurate oxygen saturation calculation even when multiple tissues are present.
3Adaptability or versatility
If the region of interest is expanded to include more tissue types, then measurement coverage is improved, but correction value consistency deteriorates
Solution Approach 1:
The patent applies dynamics by making the region of interest adjustable rather than fixed. The processor dynamically changes the region of interest based on detected tissue type and angle of view variations, allowing the correction value calculation to adapt to different imaging conditions while maintaining consistency and accuracy.
Solution Approach 2:
The patent implements local quality by calculating different correction values for different tissue types within the same region of interest. Each tissue type receives its specific correction treatment based on its optical properties, allowing accurate oxygen saturation calculation even when multiple tissues are present.
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 enables accurate oxygen saturation calculation even when multiple tissues are present in the region of interest, reducing errors and providing reliable oxygen saturation imaging by accounting for variations in tissue type and angle of view.
Implementation Method 1
a first image signal from a first wavelength range having sensitivity to blood hemoglobin; acquire a second image signal from a second wavelength range different in sensitivity to a specific pigment from the first wavelength range and different in sensitivity to the blood hemoglobin from the first wavelength range
Data Source
AI summary
Through a correction value calculation operation, a specific pigment concentration is calculated from each of respective image signals corresponding to a first wavelength range having sensitivity to blood hemoglobin, a second wavelength range different in sensitivity to a specific pigment from the first wavelength range and different in sensitivity to blood hemoglobin from the first wavelength range, a third wavelength range having sensitivity to blood concentration, and a fourth wavelength range having a longer wavelength than the first to third wavelength ranges, and is stored. A representative value is set from a plurality of specific pigment concentrations, an oxygen saturation corrected for the specific pigment is calculated, and an image display is performed using the oxygen saturation.