Real-time Lumen Distance Calculation via 3D A-line Signal Data
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Solution Overview
Problem
Current methods for detecting lumen borders in OCT images during vascular diagnosis are inefficient, often failing to provide reliable measurements due to image overload and sensitivity to catheter position, especially in real-time applications, and struggle with artifacts like stent struts and guide wires.
Innovation Solution
A method for real-time lumen distance calculation using 3D A-line signal data, which processes A-line cross sections efficiently, allowing for precise measurements by detecting vessel lumen borders and correcting for artifacts, thereby improving image interpretation and measurement accuracy across the entire OCT pullback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for detecting lumen borders in OCT images are used, then image interpretation is provided, but measurement reliability deteriorates due to image overload and sensitivity to catheter position
Solution Approach 1:
The patent segments the OCT image data into A-lines (one-dimensional depth profiles) and processes them individually to detect lumen borders. This segmentation approach reduces the complexity of processing entire images and enables more reliable border detection by focusing on fundamental signal components rather than being overwhelmed by complete image data.
Solution Approach 2:
The patent transforms the problem from two-dimensional image processing to one-dimensional A-line signal processing. By working with the depth profile signals (A-lines) rather than complete cross-sectional images, the system achieves more reliable lumen border detection while reducing computational complexity and avoiding image overload issues.
2Productivity
If real-time lumen border detection is implemented, then measurement speed is improved, but computational time increases due to processing complexity
Solution Approach 1:
By segmenting the imaging data into individual A-lines and processing them independently, the patent enables real-time detection while reducing computational burden. The one-dimensional signal processing of A-lines is computationally lighter compared to processing complete two-dimensional OCT images, thus achieving speed without excessive time loss.
Solution Approach 2:
The patent processes only the essential A-line signal data rather than entire OCT images, applying partial processing to achieve real-time performance. This selective processing of fundamental signal components enables fast lumen border detection without the computational overhead of complete image analysis.
3Loss of information
If lumen border detection is performed on complete OCT images, then comprehensive information is obtained, but measurement precision deteriorates due to artifacts like stent struts and guide wires
Solution Approach 1:
The patent segments the detection task by processing A-lines individually, which isolates the lumen border detection from interfering artifacts present in complete OCT images. This segmentation approach maintains information relevance by focusing on depth-profile signals that directly represent tissue interfaces, while filtering out spurious information from artifacts like stent struts and guide wires.
Solution Approach 2:
The patent extracts and processes only the essential A-line signal components that contain lumen border information, separating this critical data from the complete OCT image data that contains artifacts. By taking out and processing only the relevant one-dimensional signal profiles, the system achieves high measurement precision while maintaining necessary information about tissue interfaces.
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
This approach enables fast and accurate lumen border detection in all OCT frames, even with noise and artifacts, providing reliable measurements and enhancing the ability to focus on specific areas of interest, such as coronary vessels, with reduced computational time and improved image clarity.
Implementation Method 1
The aim of the OCT techniques is to measure the time delay of light by using an interference optical system or interferometry, such as via Fourier Transform or Michelson interferometers
Implementation Method 2
The frequency of the interference patterns corresponds to the distance between the sample arm and the reference arm. The higher frequencies are, the more the path length differences are
Data Source
AI summary
One or more devices, systems, methods, and storage mediums for optical imaging medical devices, such as, but not limited to, Optical Coherence Tomography (OCT), single mode OCT, and/or multi-modal OCT apparatuses and systems, and methods and storage mediums for use with same, for calculating lumen distance(s), including based on real-time A-line signal(s), are provided herein. Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, for Gastro-intestinal, cardio and/or ophthalmic applications, and being obtained via one or more optical instruments, such as, but not limited to, optical probes, catheters, capsules and needles (e.g., a biopsy needle). Fast A-line lumen segmentation methods, which can be applied real-time to a whole arterial pullback, and devices, systems, and storage mediums for use with same, are provided herein. Techniques provided herein also improve processing efficiency and decrease calculations while achieving measurements that are more precise.