Adaptive Bandwidth Quantitative Ultrasound for Deep Tissue Imaging
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Solution Overview
Problem
Current quantitative ultrasound (QUS) methods face limitations in accurately characterizing biological and non-biological materials due to noise interference and attenuation, which affect the signal-to-noise ratio (SNR) as ultrasound signals travel deeper, leading to reduced accuracy in material typing and imaging.
Innovation Solution
The integration of adaptive-bandwidth methods that adjust the spectral content computation based on the signal-to-noise ratio (SNR), combining statistics of envelope detected echo signals with normalized power spectra and global variables, enhances the accuracy of QUS results by focusing on noise-free data for classification and imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If QUS methods use fixed bandwidth spectral analysis, then processing is simple, but accuracy decreases due to noise interference at deeper depths
Solution Approach 1:
The patent implements adaptive bandwidth spectral analysis where the bandwidth is dynamically adjusted based on the signal-to-noise ratio (SNR) at different depths. The system automatically selects optimal bandwidth parameters for each depth region, transitioning from fixed to dynamic processing to maintain accuracy while managing complexity through automated adaptation.
Solution Approach 2:
The patent changes the spectral analysis bandwidth parameter adaptively based on SNR conditions. By modifying the bandwidth parameter according to signal quality at different depths, the system optimizes measurement precision without requiring manual intervention, resolving the contradiction between simplicity and accuracy.
2Loss of information
If QUS analysis includes all frequency bandwidth, then data utilization is maximized, but noise interference increases reducing SNR
Solution Approach 1:
The patent applies different bandwidth filtering characteristics to different frequency regions and depth regions based on local SNR conditions. Rather than uniform processing, the system tailors the spectral analysis bandwidth to local signal quality, preserving useful information while rejecting noise in specific frequency-depth regions.
Solution Approach 2:
The system dynamically changes the effective bandwidth parameter based on measured SNR, allowing optimal utilization of signal information at each depth while automatically excluding frequency ranges dominated by noise, thus balancing information retention with noise rejection.
3Length of stationary object
If ultrasound signals are used for deep tissue imaging, then imaging depth increases, but signal attenuation reduces SNR
Solution Approach 1:
The patent implements depth-adaptive spectral analysis where the bandwidth parameter dynamically adjusts with depth. At shallow depths with high SNR, wider bandwidth is used for maximum information extraction. At greater depths where attenuation reduces SNR, the bandwidth automatically narrows to preserve measurement accuracy, enabling reliable deep tissue imaging.
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 accuracy of material classification and imaging by eliminating noise interference and adapting to varying SNR conditions, resulting in more precise characterization and localization of materials, such as cancerous tissues and non-biological structures.
Implementation Method 1
echo signals result from spatial variations in the acoustical impedance of the material
Implementation Method 2
backscattered from biological or non-biological materials
Implementation Method 3
statistics of the envelope of linearly amplified, radio-frequency ultrasound echo signals
Implementation Method 4
estimates based on variables of normalized power spectra of linearly amplified, radio-frequency ultrasound echo signals
Data Source
AI summary
An ultrasonic material-evaluation or classification method using spectral and envelope-statistics variables from backscattered ultrasound echo signals using an adaptive-bandwidth and combined with global variables. This classification method can be applied to any organ or tissue among biological materials and any non-biological material that produces backscattered signals as a result of microscopic internal in homogeneities such as a crystalline structure.