Intraluminal Imaging Anomaly Detection via Lumen Area Curve Analysis
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Solution Overview
Problem
Current intraluminal imaging systems struggle to detect post-treatment anomalies such as stent dog-boning, suboptimal stent coverage, and diffuse disease, which are difficult to visualize and require time-consuming, subjective identification.
Innovation Solution
An intraluminal treatment anomaly detection system that uses a processor circuit to receive and analyze intravascular images, compute measurements, and generate graphical representations of lumen area changes to automatically detect conditions like stent dog-boning, under-dilation, and anatomical tapering, providing fast and systematic detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection algorithms are implemented, then detection speed and consistency improve, but system complexity increases
Solution Approach 1:
The detection system is divided into separate functional modules: image acquisition module, curve generation module, anomaly detection module, and visualization module. Each module performs a specific task, making the overall complex system manageable and maintainable while achieving fast automated detection of intraluminal anomalies.
Solution Approach 2:
A curve representing lumen area changes is introduced as an intermediary between the raw intraluminal images and the anomaly detection process. This curve serves as a simplified representation that facilitates automated analysis while reducing the complexity of directly analyzing complex medical images.
2Measurement precision
If visual identification is used, then system simplicity is maintained, but detection accuracy and objectivity deteriorate
Solution Approach 1:
The manual visual identification process is replaced with automated computational algorithms that analyze the curve data. This substitution eliminates subjectivity and improves detection accuracy for anomalies like stent dog-boning and under-expansion, while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The system transforms visual image data into quantitative curve parameters (lumen area changes along the intraluminal space). This parameter transformation enables objective, precise automated detection of anomalies that are difficult to identify visually, improving measurement precision without requiring overly complex imaging hardware.
3Reliability
If detailed analysis of multiple parameters is performed, then detection reliability improves, but processing time increases
Solution Approach 1:
The system extracts only the essential feature (lumen area changes) from the complex intraluminal images to create a simplified curve representation. This extraction maintains detection reliability for key anomalies like stent expansion issues while significantly reducing processing time compared to analyzing all image parameters in detail.
Solution Approach 2:
The system performs analysis at multiple levels: generating the complete lumen area curve for overall assessment, and then focusing detailed analysis only on segments where anomalies are suspected. This partial excessive action ensures reliable detection without unnecessarily processing the entire dataset at maximum detail, optimizing the balance between reliability and processing time.
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
The system enables accurate and timely detection of treatment anomalies, reducing clinical time and improving treatment decisions by transforming a subjective process into a quantitative, repeatable one, enhancing the precision of medical imaging.
Implementation Method 1
The transducers emit ultrasonic energy and receive ultrasound echoes reflected from the vessel
Data Source
AI summary
Disclosed is an intravascular imaging system, including a processor circuit configured for communication with an intravascular imaging catheter that is sized and shaped for positioning within a lumen of a blood vessel. The processor circuit configured to receive a plurality of intravascular images obtained by the intravascular imaging catheter while the intravascular imaging catheter is positioned within the lumen, wherein the plurality of intravascular images corresponds to a plurality of locations along a length of the blood vessel. The processor is further configured to determine a measurement associated with the lumen for each image of the plurality of intravascular images, generate a curve representative of a change in the measurement along the length of the blood vessel, detect a condition of the blood vessel based on the curve, and display a graphical representation of the condition.