Ultrasound Tissue Nonlinearity for Accurate Fat Fraction Estimation
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Solution Overview
Problem
Existing ultrasound-based methods for estimating fat fraction in tissues, such as liver fat fraction, are not sufficiently accurate and do not fully address the complexities of tissue properties, particularly in conditions like nonalcoholic fatty liver disease (NAFLD).
Innovation Solution
A method and system that utilize a combination of ultrasound-based scattering, shear wave propagation, and tissue non-linearity response measurements to estimate fat fraction, incorporating additional parameters like attenuation and speed of sound, and employ machine learning or linear models to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ultrasound-based scatter and shear wave measurements are used to estimate fat fraction, then availability and cost are improved, but measurement precision is insufficient
Solution Approach 1:
The patent combines multiple ultrasound measurement techniques (scatter measurements, shear wave measurements, and non-linear response measurements) into a unified fat fraction estimation system. By merging these different measurement modalities, the system achieves higher measurement precision while maintaining the advantages of ultrasound availability and cost-effectiveness.
Solution Approach 2:
The patent measures tissue non-linear response by transmitting ultrasound at different power levels and analyzing the non-linear relationship between transmit power and received signal amplitude. This parameter-based approach (using power level variations) enables more precise fat fraction estimation compared to traditional single-parameter ultrasound methods.
2Device complexity
If simple ultrasound-based shear wave imaging is used to estimate fat fraction, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent merges shear wave imaging with scatter measurements and non-linear response measurements. This combination allows the system to maintain relative simplicity while achieving superior measurement precision through the complementary information provided by multiple measurement techniques.
Solution Approach 2:
The patent uses tissue non-linear response as an intermediary measurement that connects ultrasound power levels to fat fraction estimation. This intermediary parameter enhances the precision of fat fraction estimation without requiring complex imaging hardware, as it can be derived from standard ultrasound transmissions at varying power levels.
3Device complexity
If traditional ultrasound attenuation and backscatter coefficients are used, then measurement process is simplified, but measurement precision of fat fraction estimation is insufficient
Solution Approach 1:
The patent introduces non-linear response measurements taken at different ultrasound power levels as an additional parameter to the traditional attenuation and backscatter coefficients. This parameter expansion enables more precise fat fraction estimation while building upon the existing simplified measurement framework rather than replacing it entirely.
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
Improves the accuracy of fat fraction estimation by integrating multiple ultrasound measurements and non-linearity responses, providing more precise diagnostic information for conditions like NAFLD.
Implementation Method 1
determining a plurality of scattering parameters of tissue
Implementation Method 2
determining a plurality of shear wave parameters of the tissue
Implementation Method 3
determine a tissue non-linearity response from the ultrasound data
Data Source
Figure 1~2
Figure 3
AI summary
For tissue property (e.g., fat fraction) estimation (34), ultrasound is used to measure (31) non-linearity response of tissue (e.g., liver tissue). The fat fraction is estimated (34) from the measured non-linearity response. The estimated fat fraction may be more accurate due to estimation (34) from the measured tissue non-linearity response. By combining with other ultrasound-based measurements, such as scatter, attenuation, and/or speed of sound, the ultrasound-based estimation (34) of fat fraction may be even more accurate. Other tissue properties may be estimated from the tissue non-linearity response alone or in combination with other measurements.