Stent Strut Detection Using OCT Shadow and Ridge Cues
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Solution Overview
Problem
Current methods for stent visualization and measurement using optical coherence tomography (OCT) are cumbersome and time-consuming, particularly in detecting stent struts and lumen boundaries, and are prone to imaging artifacts such as non-uniform rotation distortion and varying tissue compositions, which affect geometrical accuracy.
Innovation Solution
The development of semi-automatic and automatic methods for stent strut detection and lumen boundary detection using computer-assisted techniques, including one-dimensional, two-dimensional, and three-dimensional cues, such as intensity profiles, shadow detection, ridge detection, and ellipsoidal modeling, to correct for imaging artifacts and improve visualization and measurement accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to mark stent struts and lumen boundary individually, then measurement accuracy can be maintained, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The patent replaces manual mechanical marking operations with automated computer-assisted detection systems that use image processing algorithms to automatically identify and mark stent struts and lumen boundaries in OCT images, significantly reducing time and operational complexity
Solution Approach 2:
The system creates digital models and representations of stent structures by processing OCT image data, generating virtual copies of the stent geometry that can be measured and analyzed without manual intervention, thereby improving efficiency while maintaining accuracy
2Productivity
If automatic detection methods are used, then productivity is improved, but measurement precision may deteriorate due to imaging artifacts
Solution Approach 1:
The patent converts the harmful effect of imaging artifacts into beneficial information by using the shadow patterns and signal characteristics caused by artifacts as additional cues for detecting stent strut locations, thereby maintaining measurement precision while achieving automatic detection
Solution Approach 2:
The system transitions from analyzing two-dimensional OCT image slices to utilizing three-dimensional spatial information and multiple imaging dimensions, allowing the detection algorithm to distinguish true stent structures from artifacts by examining patterns across multiple spatial dimensions
3Measurement precision
If multiple detection cues are integrated, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple detection cues (intensity profiles, shadow detection, ridge detection, and ellipsoidal modeling) into a unified computer-assisted detection framework that processes all cues simultaneously through integrated algorithms, improving accuracy while managing system complexity through unified processing
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
These methods enable efficient and accurate detection and measurement of stent struts, malapposition, neointima growth, and restenosis, reducing human error and improving clinical decision-making by providing reliable positional and measurement data for stent characterization.
Implementation Method 1
Optical coherence tomography (OCT) is an interferometric imaging technique with widespread applications in ophthalmology, cardiology, gastroenterology and other fields of medicine. The ability to view subsurface structures with high resolution (2 - 15 μm) through small-diameter fiber-optic probes makes OCT especially useful for minimally invasive imaging of internal tissues and organs.
Data Source
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AI summary
In one embodiment, the invention relates to a processor based method for generating positional and other information relating to a stent in the lumen of a vessel using a computer. The method includes the steps of generating an optical coherence image data set in response to an OCT scan of a sample containing at least one stent; and identifying at least one one-dimensional local cue in the image data set relating to the position of the stent.