Pleural Line Detection Using Rib Shadow ROI Selection
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Solution Overview
Problem
Current methods for automatically detecting the pleural line in lung ultrasound images are time-consuming, operator-dependent, and lack transparency, with existing techniques requiring significant computational resources or large datasets, and face challenges in portable point-of-care systems and regulatory acceptance.
Innovation Solution
A method and system for automated pleural line detection that involves selecting specific regions of interest (ROIs) based on rib shadow analysis, computing intensity projections, and using motion maps to identify the pleural line, reducing operator dependence and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ultrasound waves are used to detect the pleural line, then detection capability is improved, but false alarms increase due to inconsistent acoustic impedance values
Solution Approach 1:
The system transitions from relying solely on acoustic impedance values to using a multi-parameter approach including time-domain analysis (time of flight, signal amplitude), frequency-domain analysis (spectral features), and spatial relationships between tissue interfaces. This parameter transformation resolves the contradiction by finding alternative detection parameters that are more reliable than acoustic impedance alone.
Solution Approach 2:
The system introduces intermediate processing steps including signal filtering, time-of-flight calculation, and correlation analysis between multiple tissue interfaces. These intermediary processes transform the raw acoustic impedance data into more reliable pleural line detection signals, reducing false alarms while maintaining detection capability.
2Measurement precision
If multiple sensing elements are used to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The probe is divided into multiple sensing elements (first, second, third, and fourth elements) that can be independently or collectively activated. This segmentation allows the system to use different element combinations for different detection scenarios, improving precision without requiring all elements to be active simultaneously, thus managing complexity.
Solution Approach 2:
The multiple sensing elements serve multiple functions: individual elements can detect local tissue characteristics, pairs of elements can determine spatial relationships and time of flight, and the collective array provides comprehensive acoustic impedance mapping. This multi-functionality achieves high detection accuracy while avoiding the complexity of dedicated separate systems for each function.
3Productivity
If real-time ultrasound imaging is performed continuously, then detection speed is improved, but energy consumption increases
Solution Approach 1:
The system performs ultrasound scanning in periodic intervals rather than continuous operation. The probe emits ultrasound waves at specific time intervals, processes the returned signals, and updates the pleural line detection. This periodic operation maintains real-time detection capability while significantly reducing energy consumption compared to continuous scanning.
Solution Approach 2:
The system maintains continuous monitoring capability through efficient signal processing that analyzes each ultrasound pulse immediately upon reception. By processing signals in real-time during each pulse interval and maintaining readiness for immediate detection, the system achieves continuous useful action without requiring continuous energy-intensive transmission.
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
Provides a uniform and efficient method for pleural line detection, reducing false positives and computational requirements, and enhancing clinical acceptance by providing transparent and reliable results.
Implementation Method 1
a sensing element of a multi-element probe is inserted into a patient's body and ultrasound waves are emitted from the sensing element into the patient's body
Implementation Method 2
acoustic impedance values are determined for a plurality of tissue interfaces based on reflected ultrasound waves detected by the sensing element
Data Source
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AI summary
:A pleural line may be determined based on initially determining a location of a rib shadow region and subsequently a rib surface. A first region of interest (ROI) may be automatically selected within an ultrasound image acquired from a lung ultrasound scan of a patient based, at least in part, on a depth of the image. The first ROI may be analyzed to determine at least one rib shadow region. The rib shadow region may be used to automatically select a second ROI. The second ROI may be analyzed to determine a location of a rib surface. The location of the rib surface may be used to automatically select a third ROI. The third ROI may be analyzed to determine the pleural line.