Shear Wave Speed Estimation Using Bayesian Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Acoustic Radiation Force (ARF) shear wave elasticity imaging methods face challenges in accurately estimating shear wave speeds due to assumptions of homogeneous, isotropic tissue, leading to biased estimates and image artifacts, especially when dealing with structured media, where small regression kernels increase noise and reduce image resolution.
Innovation Solution
The method involves determining a prior probability density function that characterizes physical properties of the target region, generating a shear wave orthogonal to the displacement, transmitting tracking pulses, and estimating propagation parameters using echo signals and statistical inference, such as Bayesian probability, to improve the accuracy of shear wave speed estimation and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If small regression kernels are used to obtain high resolution, then spatial resolution is improved, but noise in the estimated shear wave speed increases
Solution Approach 1:
The method applies preliminary filtering to the wave arrival time estimates before inputting them into the regression algorithm. By preprocessing the data to reduce noise and correct timing errors, the system can use small regression kernels for high spatial resolution without suffering from excessive noise amplification that would normally occur with small kernels.
Solution Approach 2:
The system uses an iterative approach where initial shear wave speed estimates are used to improve wave arrival time estimates, which in turn refine the speed estimates. This feedback loop allows the system to achieve accurate results with small kernels by continuously improving the quality of input data based on previous estimates.
2Device complexity
If time-of-flight reconstruction techniques are used assuming homogeneous isotropic tissue, then reconstruction simplicity is improved, but estimation accuracy deteriorates due to biased estimates and image artifacts
Solution Approach 1:
The method applies different processing treatments to different regions of the data based on local characteristics. By identifying and separately processing regions with different propagation directions or tissue properties, the system maintains algorithmic simplicity while improving accuracy in heterogeneous tissues through localized adaptive processing.
Solution Approach 2:
The reconstruction process is divided into separate stages: wave arrival time estimation, noise filtering, and regression analysis. Additionally, the data can be segmented by propagation direction or region, allowing the simple time-of-flight approach to be applied to each segment independently while correcting for directional biases.
3Productivity
If linear regression is used as a maximum likelihood estimator, then computational efficiency is improved, but measurement accuracy deteriorates when wave propagation assumptions are violated
Solution Approach 1:
The system introduces intermediate processing steps between data acquisition and final regression analysis. Wave arrival time estimates are refined through filtering and correction procedures that act as intermediaries, improving the quality of input data for the computationally efficient linear regression without requiring complex alternative algorithms.
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 reduces noise and bias in shear wave speed estimates, allowing for smaller regression kernels to be used, thereby enhancing spatial resolution and image quality while maintaining accurate mechanical parameter determination.
Implementation Method 1
Acoustic Radiation Force (ARF) shear wave elasticity imaging methods typically use a transverse propagation velocity of mechanical shear waves in materials to estimate mechanical properties of a sample
Implementation Method 2
transmitting tracking pulses in the target region; receiving corresponding echo signals for the tracking pulses in the target region
Data Source
AI summary
Methods, systems and computer program products for determining a mechanical parameter for a sample having a target region, include determining a prior probability density function that characterizes at least one physical property of the target region; generating a displacement of the sample in the target region to form a shear wave that propagates orthogonal to a direction of the displacement; transmitting tracking pulses in the target region; receiving corresponding echo signals for the tracking pulses in the target region at a plurality of lateral positions; estimating a propagation parameter of the shear wave in response to the echo signals and the prior probability density function using statistical inference; and determining at least one mechanical parameter of the target region based on the estimated propagation parameter.