Acoustic Streaming Blood Pool Detection
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Solution Overview
Problem
Current methods for detecting blood pools in trauma patients, such as ultrasound, are operator-dependent, time-consuming, and prone to false positives, especially in emergency situations, and require advanced training, making them inefficient for quick and accurate diagnosis.
Innovation Solution
The use of acoustic streaming testing, combined with advanced image processing of B-mode ultrasound images, to selectively induce and measure tissue movement, allowing for the detection of smaller blood volumes and reducing the need for high power expenditure, thereby enabling portable and efficient blood pool identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ultrasound imaging is used for blood pool detection, then blood pools can be detected, but the detection is operator-dependent and time-consuming
Solution Approach 1:
The system performs automatic blood pool detection using acoustic streaming analysis without requiring operator interpretation of images. The processor automatically analyzes tissue movement patterns and identifies blood pools, making the system self-sufficient and eliminating operator dependency while maintaining rapid examination capability
Solution Approach 2:
The system changes the detection parameter from static ultrasound image analysis to dynamic acoustic streaming-induced tissue movement analysis. By applying acoustic power and measuring the resulting tissue displacement, the system achieves automated, objective detection that is both rapid and accurate
2Measurement precision
If high power ultrasound is used to induce acoustic streaming, then tissue movement can be detected, but energy consumption increases
Solution Approach 1:
The system applies acoustic power selectively only to regions suspected of containing blood pools, rather than uniformly across the entire examination area. This partial action approach reduces overall energy consumption while maintaining sufficient tissue movement induction for accurate detection in the regions of interest
Solution Approach 2:
The acoustic power application and detection are localized to specific regions of interest identified through preliminary imaging or clinical suspicion. By concentrating the acoustic energy only where blood pools are suspected, the system achieves high detection sensitivity locally while minimizing total energy consumption
3Measurement precision
If multiple views are obtained to detect small blood volumes, then detection sensitivity improves, but examination time increases
Solution Approach 1:
The automatic acoustic streaming analysis system processes and interprets data from multiple views simultaneously without requiring additional manual examination time. The processor automatically integrates information from different angles and positions, providing comprehensive small blood pool detection while maintaining rapid overall examination throughput
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 allows for rapid, accurate, and efficient detection of blood pools, reducing operator dependency and false positives, and is suitable for use in emergency situations, even in remote areas with limited resources.
Implementation Method 1
Acoustic streaming can be described as the bulk movement of fluid in a sound field created by transfer of energy from the acoustic wave to the medium due to absorption and reflection
Implementation Method 2
bulk movement of fluid in a sound field created by transfer of energy from the acoustic wave to the medium due to absorption and reflection
Data Source
AI summary
Ultrasound-based acoustic streaming for deciding whether material is fluid is dependent upon any one or more of a variety of criteria. Examples are displacement, speed, temporal or spatial flow variance, progressive decorrelation, slope or straightness of accumulated signal to background comparisons over time, and relative displacement to adjacent soft tissue. Echogenicity-based area identification is combinable with the above movement characteristic detection in the deciding. Fluid pool identification is performable from the area-limited acoustic streaming testing and ultrasound attenuation readings. Candidates from among the areas are screenable based on specific shapes or bodily organs detected. Natural flow can be excluded from streaming detection by identification of blood vessels. Processing for each FAST ultrasound view, or for the entire procedure, is performable automatically, without need for user intervention or with user intervention to identify suspected areas.