Color Flow Imaging Signal Processing for Motion Artifact Separation
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Solution Overview
Problem
Conventional methods for color flow imaging in medical ultrasound struggle to accurately distinguish between blood flow signals and motion artifacts, particularly when they have similar characteristics, leading to incorrect processing results.
Innovation Solution
A method that involves transmitting and receiving ultrasonic pulses, performing beamforming and quadrature demodulation to acquire complex signals, followed by characteristic estimation, wall filtering, and dynamic threshold-based processing to separate and synthesize typical flow signals from similar signals, effectively distinguishing between blood flow and motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for color flow imaging, then the processing is simpler, but the accuracy of distinguishing blood flow signals from motion artifacts deteriorates
Solution Approach 1:
The patent segments the complex signal processing into distinct functional modules: beamforming module, quadrature demodulation module, characteristic estimation module, wall filtering module, and synthesis module. Each module performs a specific function in the signal processing chain, making the overall complex process more manageable and implementable while maintaining high accuracy in distinguishing blood flow from motion artifacts
Solution Approach 2:
The patent introduces characteristic estimation parameters as intermediary variables that bridge the raw complex signal and the final flow identification. These parameters (including phase, amplitude, and spectral characteristics) serve as intermediate representations that enable accurate discrimination between blood flow and motion artifacts without requiring direct complex signal manipulation
2Object-affected harmful factors
If wall filtering is applied to remove low-speed tissue information, then motion artifacts are reduced, but some low-speed blood flow signals may be lost
Solution Approach 1:
The patent applies different processing characteristics to different signal components based on their local properties. By analyzing characteristic estimation parameters such as spectral width, phase variance, and signal intensity patterns, the system identifies and preserves low-speed blood flow signals that exhibit flow-specific local characteristics while filtering out motion artifacts with different local quality patterns
Solution Approach 2:
The patent dynamically adjusts processing parameters based on the estimated characteristic parameters of the signal. By monitoring changes in spectral characteristics and phase information, the system adapts the wall filtering strength to preserve genuine low-speed flow while removing motion artifacts, rather than applying fixed filtering thresholds
3Measurement precision
If dynamic threshold values are used for flow identification, then the accuracy of flow signal detection is improved, but the computational complexity increases
Solution Approach 1:
The patent applies dynamic thresholding selectively to specific characteristic estimation parameters rather than all signal components. By focusing computational resources on the most discriminative parameters (such as phase variance and spectral width) that provide the greatest separation between flow and non-flow signals, the system achieves high detection accuracy with reduced computational overhead compared to exhaustive thresholding of all signal features
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 enhances the accuracy of identifying blood flow signals by categorizing signals into independent and similar types, allowing for precise separation and integration of flow signals, resulting in improved diagnostic imaging by reducing motion artifact interference.
Implementation Method 1
transmitting a pulse; receiving a pulse echo
Implementation Method 2
receiving a pulse echo
Implementation Method 3
performing beamforming on the received pulse echo
Implementation Method 4
color flow imaging
Data Source
AI summary
Methods and systems for color flow imaging are provided.