TCD Emboli Detection With Artifact Rejection for Real-Time Monitoring
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Solution Overview
Problem
Conventional emboli detection techniques using transcranial Doppler ultrasound (TCD) suffer from inaccuracies in embolic load detection due to miscounting of emboli and artifacts, leading to unreliable assessment of brain injury risk, particularly in real-time monitoring and varying patient populations.
Innovation Solution
Developed computational techniques that analyze an oscillating background signal representative of blood flow to identify candidate embolic regions, apply artifact rejection processes, and segment embolic regions to enhance accuracy, compatible with single-frequency TCD devices, enabling real-time emboli detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional TCD ultrasound techniques are used for emboli detection, then real-time monitoring capability is achieved, but measurement precision deteriorates due to miscounting of emboli and artifacts
Solution Approach 1:
The patent segments the detection process into distinct phases: background signal determination, candidate embolic region identification, and artifact rejection. This segmentation allows each phase to be optimized independently, improving overall measurement precision by systematically addressing different sources of error at appropriate stages.
Solution Approach 2:
The patent performs preliminary determination of the background signal representative of normal blood flow before identifying candidate embolic regions. This preliminary action establishes a reference baseline that enables more accurate distinction between normal flow variations and actual emboli, thereby improving detection accuracy.
2Loss of information
If conventional TCD ultrasound techniques are used for emboli detection, then detection capability is achieved, but loss of information increases due to undercounting and overcounting of emboli
Solution Approach 1:
The patent implements feedback mechanisms where detected candidate embolic regions are evaluated against the background signal, and artifact rejection outcomes feed back into the detection process. This feedback loop continuously refines the detection accuracy, reducing information loss from miscounting while maintaining real-time monitoring capability.
Solution Approach 2:
The patent replaces manual emboli counting with automated computational analysis of ultrasound signals. This substitution eliminates human error in counting while maintaining high processing speed, thereby reducing information loss without sacrificing productivity.
3Measurement precision
If simple emboli detection methods are used, then ease of operation is maintained, but measurement precision deteriorates due to inability to distinguish artifacts from emboli
Solution Approach 1:
The patent introduces an intermediary background signal that represents normal blood flow patterns. This intermediary serves as a reference mediator between the raw ultrasound signal and emboli identification, enabling more accurate distinction between artifacts and true emboli without requiring overly complex analysis systems.
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
Improves the accuracy and reliability of emboli detection, reducing undercounting and overcounting, and allows for real-time monitoring of embolic load, facilitating timely interventions for brain injury prevention.
Implementation Method 1
As a result of the Doppler effect, the frequencies of the echoes can be used to determine the direction and speed of the blood flow
Implementation Method 2
A TCD device has an ultrasound probe that emits high-frequency sound waves (∼2 MHz) and a sensor that detects echoes from the sound waves
Data Source
AI summary
Techniques for detecting embolic information for a patient. The techniques may include obtaining data identifying an ultrasound signal associated with the patient, identifying a set of candidate embolic regions in the data, identifying a set of embolic regions from among the set of candidate embolic regions, and outputting embolic information corresponding to the set of embolic regions.