Ultrasonic Doppler Flow Imaging Using Eigen Decomposition
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Solution Overview
Problem
Traditional color Doppler ultrasound struggles to accurately observe small vessels and slow blood flow in parenchymal organs due to tissue and flow signal overlap, leading to discontinuous and unstable flow signals, necessitating invasive contrast agent injection.
Innovation Solution
An ultrasonic Doppler flow imaging method using eigen decomposition to separate eigenvectors matrices into high, medium, and low-power matrices, calculating signal-to-noise ratios, and applying an opening and closing function with an X-Shape element to extract reliable flow data and eliminate noise, resulting in clear and continuous flow images without contrast agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional wall filter is used to filter out tissue signals, then tissue signals are removed, but flow signals cannot be completely separated when flow velocity is low, resulting in discontinuous and unstable flow signals
Solution Approach 1:
The patent segments the eigenvector matrices into three distinct groups based on power values: high-power matrices containing primarily tissue signals, medium-power matrices containing flow signals, and low-power matrices containing noise signals. This segmentation allows selective processing of each group to achieve both tissue rejection and complete flow signal retention.
Solution Approach 2:
Different processing strategies are applied to different power groups: high-power matrices undergo threshold processing to remove tissue signals, medium-power matrices are retained to preserve flow signals, and low-power matrices are processed to eliminate noise. This localized quality approach optimizes each segment's contribution to the final flow image.
2Measurement precision
If contrast agent is injected to enhance reflections, then flow detection accuracy is improved, but the approach becomes invasive
Solution Approach 1:
The patent extracts flow signal information directly from the medium-power eigenvector matrices through eigen decomposition, eliminating the need for contrast agents. By taking out and processing only the relevant flow-containing components, the method achieves accurate flow detection without invasive interventions.
Solution Approach 2:
The patent replaces the mechanical/chemical approach of contrast agent injection with a signal processing approach based on eigen decomposition and power-based filtering. This substitution maintains measurement precision while eliminating the harmful invasive factor.
3Reliability
If eigen decomposition is used to separate eigenvector matrices into high, medium, and low-power matrices, then tissue and noise signals are filtered, but the processing complexity increases
Solution Approach 1:
The patent changes the parameter used for signal separation from traditional frequency-based filtering to power-based sorting of eigenvector matrices. By sorting matrices according to their power values and assigning them to different groups, the method achieves effective signal separation with a systematic approach that manages processing complexity.
Solution Approach 2:
The eigen decomposition process inherently provides the power values needed for classification, and the method uses these same decomposed matrices for both tissue signal identification and flow signal extraction. This self-service approach reduces the need for additional separate processing steps.
4Loss of information
If all eigenvector matrices are retained for flow analysis, then complete flow data is preserved, but noise signals and tissue signals contaminate the flow image
Solution Approach 1:
The patent segments all eigenvector matrices into three power-based groups and selectively processes each group. This segmentation preserves complete flow data from the medium-power matrices while filtering out tissue signals from high-power matrices and noise signals from low-power matrices, achieving both completeness and quality.
Solution Approach 2:
Different quality standards and processing approaches are applied to different power groups: high-power matrices receive aggressive filtering to remove tissue contamination, medium-power matrices are preserved with minimal processing to maintain flow signal integrity, and low-power matrices undergo noise reduction processing.
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 method provides noninvasive, accurate, and stable flow imaging by filtering tissue and noise signals, enabling complete retention of flow data and enhancing imaging quality without the need for contrast agents.
Implementation Method 1
Traditional color Doppler ultrasound has its limitations in observing small vessels and slow blood flow
Data Source
AI summary
Disclosed is an ultrasonic Doppler flow imaging method. The method includes: using eigen decomposition on an ultrasonic image to obtain eigenvectors matrices with different power values, so that the SNR and power distribution of each point of the ultrasonic image are calculated, thereby accurately extracting flow data with high reliability and obtaining accurate and stable flow images. The method has the beneficial effects of filtering out tissue signals and noise signals on the characteristic dimension, so as to better extract flow data by using eigen decomposition on ultrasonic images, retaining complete flow data and providing stable and clear imaging of fine flow without the need of contrast agent injection that may cause injuries to human bodies.