Intravascular Lesion Detection Using Predicted EEL Values
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Solution Overview
Problem
The presence of plaque or buildup in blood vessels hinders the automatic determination of measurements during intravascular imaging, leading to incomplete data and inaccurate identification of candidate treatment zones, necessitating manual intervention by physicians.
Innovation Solution
The method predicts the external elastic lamina (EEL) values for image frames where they cannot be detected, using a representative EEL value derived from a threshold number of frames with low plaque burden, intima and media thickness, or proximal/distal vessel segments, allowing for accurate plaque burden determination and lesion identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If plaque burden is high in image frames, then EEL value cannot be automatically determined, but this leads to incomplete vessel data and prevents accurate lesion identification
Solution Approach 1:
The system performs preliminary actions by identifying and setting aside image frames with high plaque burden that prevent EEL determination, then proceeds to determine EEL values for frames with lower plaque burden first. These preliminary determinations are used to establish representative EEL values that will later be applied to the frames where direct determination failed, thus resolving the information loss.
Solution Approach 2:
The system introduces an intermediary approach by using representative EEL values derived from frames with detectable EEL as a mediator. These representative values serve as proxies for frames where EEL cannot be directly determined due to high plaque burden, enabling indirect estimation and maintaining data completeness without requiring direct measurement in all frames.
2Loss of information
If manual intervention is required to enter or adjust clinical parameters, then data completeness improves, but this increases time consumption and reduces productivity
Solution Approach 1:
The system implements self-service by automatically determining EEL values for the majority of frames and using representative EEL values to fill in gaps where direct determination failed. This automated self-correction and self-completion approach eliminates the need for manual physician intervention in most cases, maintaining data completeness while preserving workflow efficiency.
Solution Approach 2:
The system performs preliminary automated processing of EEL determination for all frames before presenting results to the physician. By pre-processing and automatically resolving EEL values where possible, the system reduces the workload and time required for manual review and adjustment, thereby maintaining productivity while ensuring data completeness.
3Loss of information
If representative EEL value is used to predict EEL for frames with high plaque burden, then data completeness improves, but this may reduce measurement precision for heavily affected vessels
Solution Approach 1:
The system applies partial action by using representative EEL values only for the specific frames where direct determination failed due to high plaque burden, while maintaining direct measurement for frames where it is feasible. This selective application ensures data availability for all frames without compromising the precision of measurements taken directly from frames with detectable EEL.
Solution Approach 2:
The system implements local quality by applying different determination strategies to different frames based on their individual characteristics. Frames with detectable EEL undergo direct precise measurement, while only frames with high plaque burden use the representative EEL estimation approach. This localized application of different methods optimizes both data availability and measurement precision for each specific frame.
Data Source
AI summary
The present disclosure provides systems and methods for determining an external elastic lamina (“EEL”) value for intravascular image frames and using the predicted EEL to determine a plaque burden for respective image frames. The predicted EEL value may be determined based on a threshold number of image frames within a region of interest having plaque burden below a threshold plaque burden. In some examples, the predicted EEL value may be determined as a function of intima thickness and media thickness and the H-K model derived expected lumen diameter. The determined plaque burden and visible EEL arc may be used to automatically identify lesions and suggest candidate treatment zones.