Ultrasound Flow Estimation Using Predictive Velocity Correction
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Solution Overview
Problem
In medical diagnostic ultrasound, setting the velocity scale for Doppler velocity or color flow velocity imaging is challenging due to the variance in maximum velocity over time, leading to poor dynamic range and sensitivity, aliasing issues, and inaccurate quantification of flow hemodynamics, especially when imaging pulsing flows like those in the circulatory system.
Innovation Solution
A predictive-based method is employed to estimate flow by using previous flow data, boundary conditions, and current conditions to correct velocity estimates, unalias velocities, and adjust settings such as the wall filter and velocity scale, allowing for improved representation of flow hemodynamics and reducing operator dependence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the velocity scale is set to be overly inclusive, then the dynamic range and sensitivity are improved, but high velocity data may be aliased
Solution Approach 1:
The system performs preliminary actions by acquiring velocity data at multiple time points before the current time point, storing these historical velocity data in a buffer, and using them to predict future flow conditions. This allows the system to anticipate aliasing issues and adjust velocity scale settings proactively, resolving the contradiction between inclusive velocity scaling and aliasing prevention.
Solution Approach 2:
The system implements feedback by continuously monitoring historical velocity data and using it to predict future flow conditions. The predicted flow information feeds back into the velocity scale adjustment mechanism, allowing dynamic adaptation of the velocity scale to prevent aliasing while maintaining sensitivity, thus resolving the contradiction between measurement precision and reliability.
2Reliability
If the velocity scale is set narrowly, then aliasing is reduced, but the dynamic range and sensitivity become poor
Solution Approach 1:
The system applies dynamics by making the velocity scale adjustable and adaptive rather than fixed. Based on predicted flow conditions derived from historical velocity data, the velocity scale dynamically adjusts to optimize both aliasing reduction and sensitivity. This dynamic adjustment resolves the contradiction between narrow scaling for aliasing reduction and broad scaling for sensitivity.
Solution Approach 2:
The system changes the parameter of velocity scale based on predicted flow conditions. By analyzing historical velocity data and predicting future flow patterns, the system modifies the velocity scale parameter to match actual flow conditions, thereby achieving both aliasing reduction and maintained sensitivity without being constrained by a fixed scale.
3Device complexity
If the velocity information is measured axially only, then the measurement process is simplified, but the accuracy of flow speed quantification deteriorates when flow is at an angle
Solution Approach 1:
The system applies dimensionality change by transitioning from one-dimensional axial velocity measurement to three-dimensional flow velocity measurement. By using predicted flow information from multiple time points and spatial locations, the system reconstructs the complete velocity vector including lateral and elevation components, thereby achieving accurate flow speed quantification regardless of flow direction while maintaining practical measurement complexity.
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 results in more accurate flow velocity information, reduces aliasing, and enhances the repeatability and reproducibility of ultrasound color flow assessments, providing a more accurate and reliable method for monitoring both short-term and long-term flow dynamics.
Implementation Method 1
For Doppler velocity or color flow velocity imaging, a velocity scale is set
Data Source
AI summary
Flow estimation is provided. The flow is predicted. A mathematical, logic, machine learning or other model is used to predict flow. For example, the boundary conditions associated with a previous flow, the previous flow, and current boundary conditions are used to predict the current flow. The current flow is corrected using the predicted flow. Velocities may be unaliased based on the predicted flow. The predicted flow may replace the current flow. Prediction may additionally or alternatively be used in determination of lateral or elevational flow.