Intravascular OCT Side Branch Detection via A-Line Segmentation
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Solution Overview
Problem
Intravascular optical coherence tomography (OCT) imaging struggles to accurately identify side branches in coronary arteries due to obscuration by guidewires, stent struts, blood, and shadows, leading to challenges in diagnosing and treating coronary artery disease effectively.
Innovation Solution
An automated method for detecting side branches using intravascular image datasets, involving lumen boundary detection, edge identification, noise floor thresholding, and branching matrix generation to isolate candidate branching regions, thereby enhancing the visibility of side branches in OCT images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCT imaging is used to visualize blood vessels, then high-resolution images of coronary artery morphology are obtained, but side branches are obscured by guidewires, stent struts, blood, and shadows making accurate identification difficult
Solution Approach 1:
The patent segments the intravascular image data into discrete A-lines (scan lines) and processes each independently to detect side branches. By dividing the continuous image into manageable units and analyzing intensity patterns in each A-line, the system can identify side branch signatures even when partially obscured by artifacts, thereby improving detection accuracy despite the presence of harmful imaging factors
Solution Approach 2:
The patent transforms the two-dimensional cross-sectional OCT images into a one-dimensional analysis along A-lines, focusing on intensity variations along the depth dimension. This dimensional reduction allows the system to detect side branches by analyzing intensity patterns along individual scan lines, making the detection process more robust against artifacts that affect specific regions of the 2D image
2Reliability
If manual identification of side branches is performed by clinicians, then treatment decisions can be made, but the process is time-consuming and prone to human error due to obscured landmarks
Solution Approach 1:
The patent implements an automated system that performs side branch detection independently without requiring manual clinician intervention. The algorithm automatically processes intravascular image datasets, detects lumen boundaries, identifies side branch signatures through intensity pattern analysis, and generates detection results, thereby eliminating time-consuming manual analysis while maintaining high reliability through consistent automated processing
Solution Approach 2:
The patent replaces the manual visual inspection process with an automated computational algorithm. Instead of clinicians manually examining OCT images to identify side branches, the system uses computer-based image processing techniques including A-line analysis, intensity thresholding, and pattern recognition to automatically detect side branches, significantly reducing analysis time while improving consistency and reliability
3Manufacturing precision
If stents are placed to treat stenosis, then blood flow is restored, but side branches may be obscured or damaged if not properly identified beforehand
Solution Approach 1:
The patent performs side branch detection before stent placement by analyzing intravascular OCT images to identify and mark the locations of side branches. This preliminary detection provides clinicians with advance knowledge of side branch positions, enabling them to plan stent placement strategies that avoid covering side branch openings, thereby preventing information loss and ensuring precise stent positioning
Solution Approach 2:
The patent provides feedback to clinicians by displaying detected side branch locations on the intravascular images. The system processes OCT data, identifies side branches through automated analysis, and presents this information in a format that guides stent placement decisions, creating a feedback loop that improves manufacturing precision by ensuring side branches are not inadvertently obscured by stent struts
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 method improves the accuracy of side branch detection, reducing false positives and enabling more precise treatment planning by providing clear visualization of side branches, even when obscured by imaging artifacts.
Implementation Method 1
Intravascular optical coherence tomography (OCT) is a catheter-based imaging modality that uses light to peer into coronary artery walls and generate images thereof for study. Utilizing coherent light, interferometry, and micro-optics, OCT can provide video-rate in-vivo tomography within a diseased vessel with micrometer level resolution.
Implementation Method 2
Utilizing coherent light, interferometry, and micro-optics, OCT can provide video-rate in-vivo tomography within a diseased vessel with micrometer level resolution.
Data Source
AI summary
In part, the disclosure relates to an automated method of branch detection with regard to a blood vessel imaged using an intravascular modality such as OCT, IVUS, or other imaging modalities. In one embodiment, a representation of A-lines and frames generated using an intravascular imaging system is used to identify candidate branches of a blood vessel. One or more operators such as filters can be applied to remove false positives associated with other detections.