Shear Wave Velocity Estimation Using RANSAC Peak Fitting
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Solution Overview
Problem
Existing methods for estimating shear wave velocity in ultrasound elastography are limited by noisy motion signals and the presence of outliers, particularly in in vivo applications where physiological motion and tissue inhomogeneity affect shear wave speed estimation.
Innovation Solution
A computer-implemented method that generates temporal and spatial peak data from elastography data, normalizes motion profiles, and uses thresholding and fitting techniques, such as RANSAC, to estimate shear wave velocity, improving robustness in noisy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional TTP method is used for shear wave velocity estimation, then the method is simple to implement, but the estimation accuracy deteriorates in the presence of noisy motion signals and physiological motion
Solution Approach 1:
The patent introduces an intermediary RANSAC algorithm that acts as a mediator between the raw noisy motion data and the final shear wave velocity estimation. This intermediary process selectively identifies and uses only the most reliable data points (inliers) while discarding outliers, thereby improving measurement precision without significantly complicating the overall implementation
Solution Approach 2:
The patent replaces the conventional mechanical/physical approach of using all measured data points directly in the TTP calculation with a computational statistical approach (RANSAC). This substitution allows the system to handle noisy data more effectively by using iterative random sampling and consensus building to identify valid measurements, thereby improving accuracy while maintaining operational simplicity
2Reliability
If RANSAC algorithm is used to improve robustness, then the estimation becomes more reliable, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by using RANSAC to process only a subset of the data (random samples) rather than all data simultaneously. The algorithm iteratively samples small portions of the data, performs linear fitting on these subsets, and accumulates results. This partial processing approach improves reliability by focusing computational effort on the most informative data points while keeping the overall computational burden manageable
Solution Approach 2:
The patent performs preliminary actions by pre-processing the motion data to identify temporal peaks and spatial peaks before applying the RANSAC algorithm. This preliminary peak detection creates a structured representation of the data that simplifies subsequent RANSAC processing, thereby improving reliability without proportionally increasing computational complexity
3Quantity of substance
If all motion data is used for velocity estimation, then the measurement coverage is maximized, but the presence of outliers degrades the estimation quality
Solution Approach 1:
The patent extracts and removes outlier data points from the full dataset before performing velocity estimation. By identifying and taking out only the problematic outliers (using RANSAC's iterative sampling to detect inconsistent measurements), the system maintains measurement coverage from the majority of valid data while eliminating the degrading effect of outliers, thereby preserving both quantity and precision
Data Source
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AI summary
Described here are systems and methods for estimating shear wave velocity from data acquired with a shear wave elastography system. More particularly, the systems and methods described here implement a spatiotemporal time-to-peak algorithm that searches for the times at which shear wave motion is at a maximum while also searching for the lateral locations at which shear wave motion is at a maximum. Motion can include displacement, velocity, or acceleration caused by propagating shear waves. A fitting procedure (e.g., a linear fit) is performed on a combined set of these temporal peaks and spatial peaks to estimate the shear wave velocity, from which mechanical properties can be computed. Motion amplitude thresholding can also be used to increase the number of points for the fitting.