Intravascular Ultrasound Lumen Border Detection
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Solution Overview
Problem
Current intravascular ultrasound (IVUS) systems require significant manual corrections for accurate lumen border detection, which is time-consuming and lacks sufficient accuracy in large patient datasets, necessitating a fully automated and high-accuracy method for vascular lumen border identification.
Innovation Solution
A method and system that process a sequence of IVUS frames by determining texture and flow features to characterize regions as within or outside the lumen, using processor-executable instructions to derive and display the lumen border, enabling automated and precise lumen border detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual corrections are used for lumen border detection, then accuracy can be improved, but procedural time increases significantly
Solution Approach 1:
The system performs preliminary automated lumen border detection using texture and flow features before manual review, pre-processing the image sequence to identify candidate borders that reduce the scope of required manual corrections
Solution Approach 2:
The system enables semi-automated detection where the automated algorithm performs the primary detection work and the operator only needs to review and correct errors, rather than performing complete manual detection
2Productivity
If fully automated detection is implemented, then productivity increases, but measurement precision may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where detection results are evaluated and used to refine subsequent detections, with operator corrections feeding back into the automated system to improve future accuracy
Solution Approach 2:
The system dynamically adjusts detection parameters such as texture analysis thresholds and flow feature weights based on image quality and vessel characteristics, optimizing the balance between speed and accuracy for different clinical scenarios
3Measurement precision
If multiple features are analyzed for each region, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The analysis is segmented into distinct stages: texture feature extraction, flow feature extraction, and integrated border detection. Each stage processes specific features independently, managing complexity through modular organization of the processing pipeline
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 provides a fully automated and accurate lumen border detection, reducing procedural time and improving workflow by exploiting texture and flow features in IVUS frames, enhancing the accuracy of vascular measurements and visualization.
Implementation Method 1
The pulse generator in the control module generates electrical pulses that are delivered to the one or more transducers and transformed to acoustic pulses that are transmitted through patient tissue. Reflected pulses of the transmitted acoustic pulses are absorbed by the one or more transducers and transformed to electric pulses.
Data Source
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AI summary
A method for processing a sequence of ultrasound frames for display includes receiving a sequence of intravascular ultrasound (IVUS) frames of a vessel having a lumen, the sequence including a first frame and a second frame; determining one or more texture features for each of one or more regions of the first frame; determining at least one flow feature for each of the one or more regions by comparing the first and second frames; deriving a lumen border for the first frame using the one or more texture features and the at least one flow feature to characterize the one or more regions as within or outside of the lumen of the vessel; and displaying an ultrasound image of the first frame with the lumen border.