Ultrasound Clutter Filtering via Rank Matrix Decomposition
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Solution Overview
Problem
Current ultrasound imaging devices face challenges in achieving real-time imaging and effective clutter filtering due to high computational loads and performance degradation in separating blood flow signals from clutter signals.
Innovation Solution
The method involves performing rank matrix decomposition on ultrasound data to generate common scale information, estimating local characteristic information by reflecting spatial information on each pixel, and using local adaptive filtering with calculated cutoff threshold values to extract blood flow signals, reducing computational load and improving filtering performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clutter filtering methods are used to separate blood flow signals from clutter signals, then filtering performance is improved, but computational load increases and real-time imaging capability deteriorates
Solution Approach 1:
The patent divides the clutter filtering process into two distinct stages: (1) a preliminary filtering stage that processes all ultrasound data to remove major clutter components, and (2) a refined filtering stage that processes only the remaining signal components. This segmentation reduces the computational burden on the second stage while maintaining overall filtering performance, thereby enabling real-time imaging capability.
Solution Approach 2:
The patent applies partial action by performing comprehensive clutter filtering only on necessary signal components rather than processing the entire ultrasound signal with full filtering complexity. The method selectively applies filtering operations to specific signal portions that require enhanced processing, leaving other components with simpler processing, thus reducing overall computational load while maintaining filtering effectiveness.
2Measurement precision
If advanced clutter filtering algorithms are implemented to improve blood flow signal separation, then filtering performance is enhanced, but device complexity increases
Solution Approach 1:
The filtering algorithm is segmented into multiple processing stages with increasing complexity. The first stage uses simpler filtering operations to remove obvious clutter, while subsequent stages apply more sophisticated algorithms only to the remaining signal components. This staged approach achieves advanced blood flow signal separation without requiring the entire system to implement the full complexity of advanced algorithms throughout.
Solution Approach 2:
The patent implements partial action by applying advanced filtering algorithms only to specific portions of the signal processing pipeline rather than uniformly across all processing stages. This selective application of complex algorithms to where they are most needed reduces overall device complexity while maintaining enhanced blood flow signal separation performance in critical areas.
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 enables real-time imaging and enhanced filtering performance by reducing computational load and effectively separating blood flow signals from clutter signals, allowing for precise imaging of small vessels.
Implementation Method 1
An ultrasound signal is transmitted to a human body, and when the transmitted signal is reflected back from blood, the reflected ultrasound signal is used
Implementation Method 2
An ultrasound Doppler method that utilizes a commonly well-known Doppler effect is used to measure blood flow information
Data Source
AI summary
An ultrasound imaging device and a clutter filtering method using the same are disclosed. The clutter filtering method using the ultrasound imaging device according to one embodiment includes obtaining ultrasound data from a field-of-view (FOV) of an object, generating decomposition data including common scale information by performing rank matrix decomposition once on all of the obtained ultrasound data, estimating local characteristic information by reflecting spatial information on each pixel to the common scale information, and extracting a blood flow signal by performing filtering on each pixel based on the estimated local characteristic information.