3D Coronary Procedure Modeling for Lesion Risk Assessment
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Solution Overview
Problem
Current medical procedures rely heavily on mental conversion of 2D angiogram images to 3D models, leading to inaccurate treatment selections and suboptimal patient outcomes due to lack of accurate vessel morphology and procedural risk analysis.
Innovation Solution
A system utilizing multiple imaging modalities to create a 3D model of coronary vasculature, incorporating vessel morphology and procedural risk assessment, and overlaying clinical guidance information on angiogram data to aid clinician decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 2D angiogram images are used for treatment planning, then the procedure can be performed with standard imaging equipment, but the accuracy of vessel morphology visualization and treatment selection deteriorates
Solution Approach 1:
The patent applies dimensionality change by converting 2D angiogram images into 3D volumetric models of coronary vasculature. Multiple 2D images from different angles are processed to reconstruct accurate 3D representations, enabling precise visualization of vessel morphology, lesions, and anatomical relationships that cannot be obtained from 2D images alone.
Solution Approach 2:
The patent introduces computer vision algorithms and image processing systems as intermediaries between the raw 2D angiogram images and the clinician's understanding of vessel anatomy. These intermediary systems automatically perform the complex task of converting 2D projections into accurate 3D models, bridging the gap between standard imaging equipment and detailed anatomical visualization.
2Measurement precision
If multiple imaging modalities and 3D modeling are implemented, then treatment selection accuracy improves, but device complexity and computational requirements worsen
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform 3D model generation, lesion detection, and treatment pathway analysis without requiring manual intervention. The computer vision algorithms autonomously process imaging data, and the system self-updates treatment recommendations based on the 3D models and risk assessments, reducing the burden on clinicians despite the increased computational complexity.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting imaging parameters and processing algorithms based on the specific patient anatomy and procedural requirements. The system adapts its computational methods and modeling parameters to optimize accuracy for different vascular conditions while managing system complexity through intelligent parameter selection and adjustment.
3Productivity
If real-time 3D modeling and risk assessment are performed during procedures, then clinical decision-making improves, but processing time and computational resources worsen
Solution Approach 1:
The patent applies preliminary action by pre-processing imaging data and generating 3D models before the actual interventional procedure begins. Risk assessments and treatment pathway analyses are performed in advance using the 3D volumetric data, allowing the interventionalist to access pre-computed information during the procedure rather than performing complex computations in real-time.
Solution Approach 2:
The patent implements continuity of useful action by maintaining updated 3D models and risk assessments throughout the procedural workflow. The system continuously refines its models as additional imaging data becomes available during the procedure, ensuring that treatment recommendations remain current and accurate without requiring repeated computational processing at critical decision points.
Data Source
AI summary
Example systems and techniques are disclosed that may determine at least one treatment strategy for a lesion. An example system may include memory configured to store a plurality of treatment pathways and processing circuitry communicatively coupled to the memory. The processing circuitry may be configured to determine the plurality of treatment pathways. The processing circuitry may be configured to determine, for each respective treatment pathway of the plurality of treatment pathways, one or more respective predicted effectiveness indicators, one or more respective predicted risks, and a respective confidence level associated with at least one of the respective predictions. The processing circuitry may be configured to output for display the plurality of treatment pathways, and the one or more respective predicted effectiveness indicators, the one or more respective predicted risks, and the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway.