Coronary Calcium Burden Mapping for Stent Under-Expansion Risk
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Solution Overview
Problem
Current methods fail to adequately quantify the risk of stent under-expansion due to coronary artery calcification, which is critical for successful treatment, as they do not provide physicians with sufficient tools to assess calcium burden and its impact on stent expansion.
Innovation Solution
A method using image processing and machine learning to detect and score calcium burden in arterial features, aligning frames from different pullbacks, and predicting stent expansion risk, with visual indications and color coding to highlight potential under-expansion areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rules-of-thumb are used to estimate under expansion risk, then the assessment process is simple, but the risk quantification is inadequate and underappreciated
Solution Approach 1:
The patent replaces manual visual estimation with automated image processing and machine learning algorithms. The system uses optical coherence tomography (OCT) images to automatically detect, segment, and quantify calcium deposits, substituting the manual mechanical assessment process with computational analysis that provides both simplicity and precision simultaneously.
Solution Approach 2:
The patent introduces an intermediary computational system that processes OCT images and generates quantitative risk assessments. This intermediary layer translates complex image data into actionable clinical insights, bridging the gap between raw imaging data and clinical decision-making while maintaining both ease of use and measurement accuracy.
2Difficulty of detecting and measuring
If current imaging modalities are used to visualize calcium deposits, then calcium visualization is achieved, but the risk remains under-quantified
Solution Approach 1:
The patent applies segmentation to divide the arterial wall into distinct tissue types by detecting and separating calcium deposits from other plaque components. This segmentation enables precise quantification of calcium burden by measuring the area and characteristics of segmented calcium regions, transforming qualitative visualization into quantitative risk assessment.
Solution Approach 2:
The patent changes the measurement parameters from simple visual detection to quantitative analysis of multiple parameters including calcium area, calcium arc, calcium thickness, and derived risk scores. This parameter transformation enables precise risk quantification while maintaining the visualization capability provided by current imaging modalities.
3Productivity
If stent expansion is not properly assessed, then the treatment proceeds quickly, but the risk of in-stent restenosis increases
Solution Approach 1:
The patent performs preliminary assessment of calcium burden and predicted stent expansion risk before stent deployment. By calculating risk scores based on pre-stenting OCT images, the system enables clinicians to prepare appropriate treatment strategies in advance, ensuring both rapid procedure execution and high treatment success rates through proactive risk management.
Solution Approach 2:
The patent provides feedback on predicted stent expansion outcomes based on calcium burden analysis. This feedback loop allows clinicians to adjust their treatment approach in real-time, selecting appropriate balloon sizes and inflation pressures to achieve optimal stent expansion while maintaining procedural efficiency and treatment reliability.
Data Source
AI summary
The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.