Ultrasonic Observation Apparatus Tissue Characterization
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Solution Overview
Problem
Current ultrasonic observation techniques face challenges in accurately distinguishing between different tissue types, such as normal and abnormal tissues, due to limitations in analyzing ultrasonic wave frequencies and their attenuation, which affects the clarity and reliability of tissue characterization.
Innovation Solution
An ultrasonic observation apparatus that analyzes ultrasonic wave frequencies using a frequency analyzing unit, extracts feature amounts by polynomial approximation of specific frequency bands, and generates feature amount images by associating spectrum intensities with visual information, enabling clear differentiation between tissue types based on size and acoustic impedance ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ultrasonic observation techniques are used, then the observation process is simple, but the ability to accurately distinguish between different tissue types is poor
Solution Approach 1:
The frequency spectrum is segmented into multiple frequency bands (first frequency band, second frequency band, etc.) with different center frequencies. Each band is analyzed separately to extract spectrum intensity features, allowing the system to capture multiple characteristics of tissue properties without overwhelming complexity in a single analysis step.
Solution Approach 2:
The system transforms the one-dimensional frequency spectrum into a multi-dimensional feature space by extracting spectrum intensities at different frequency bands and combining them with attenuation information. This dimensional expansion enables better tissue differentiation while maintaining manageable system complexity through structured feature extraction.
2Measurement precision
If frequency analysis is performed to improve tissue differentiation, then tissue characterization improves, but the influence of ultrasonic wave attenuation increases
Solution Approach 1:
The system extracts spectrum intensity features from specific frequency bands and separates this information from the attenuation effects. By taking out the frequency-specific spectrum intensities as independent features and combining them with attenuation data, the system can differentiate tissue types while compensating for attenuation influences rather than being overwhelmed by them.
Solution Approach 2:
The system changes the analysis parameters by using multiple frequency bands with different center frequencies and extracting spectrum intensities at specific frequencies within each band. This parameter variation allows the system to capture tissue characteristics across different frequency ranges while managing attenuation effects through structured multi-parameter analysis.
3Measurement precision
If multiple frequency bands are analyzed to extract comprehensive tissue features, then tissue characterization accuracy improves, but computational complexity increases
Solution Approach 1:
The computational task is segmented into separate frequency band analyses, each processing a specific portion of the frequency spectrum. By dividing the overall analysis into manageable segments (first frequency band, second frequency band, etc.), the system extracts comprehensive tissue features while keeping individual computational steps less complex and more efficient.
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 apparatus effectively generates feature amount images that visually indicate tissue characterization, allowing for accurate differentiation between normal and abnormal tissues, and reduces the influence of ultrasonic wave attenuation, improving diagnostic accuracy.
Implementation Method 1
transmits an ultrasonic wave to a specimen and receives the ultrasonic wave reflected from the specimen
Data Source
AI summary
An apparatus includes a unit that analyzes frequencies of an ultrasonic wave at a plurality of points to calculate a frequency spectrum of the points; a unit that approximates a portion included in a frequency band between a first frequency and a second frequency larger than the first frequency, by a polynomial, in the frequency spectrum of the points to extract first spectrum intensity which is a value of the polynomial in a third frequency included in a domain of the polynomial and second spectrum intensity which is a value of the polynomial in a fourth frequency different from the first to third frequencies, as a feature amount; and a unit that associates the first spectrum intensity, the second spectrum intensity, and/or a function of difference or ratio between the first and second spectrum intensity, to generate a feature amount image to indicate a distribution of the feature amount.