Coronary Intervention Planning Using CT Plaque Mechanical Simulation
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Solution Overview
Problem
Current coronary interventions, such as stenting, lack sufficient information on plaque location and composition for effective planning and guidance, leading to suboptimal outcomes.
Innovation Solution
Utilizing non-invasive CT plaque characterization through coronary CT angiography for pre-operative planning, including Hounsfield unit, mono-energetic, Zeff, and iodine content imaging, to generate mechanical models that simulate potential interventions and guide device selection and treatment parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive coronary angiography and intravascular imaging are used for intervention guidance, then measurement precision of plaque location and composition is improved, but device complexity and procedural invasiveness increase
Solution Approach 1:
The patent introduces a computational model as an intermediary that processes non-invasive CT imaging data to generate virtual angiograms and plaque composition information. This mediator translates simple non-invasive images into detailed diagnostic information that would otherwise require complex invasive imaging systems, thereby achieving high measurement precision without increasing device complexity or procedural invasiveness.
Solution Approach 2:
The patent replaces mechanical invasive imaging systems (catheters, intravascular ultrasound probes) with a computational approach that uses non-invasive CT data combined with mechanical models of the coronary artery. This substitution eliminates the need for physical invasive devices while maintaining or improving measurement precision through virtual simulations of plaque location and composition.
2Ease of operation
If non-invasive CT imaging is used for plaque characterization, then ease of operation and patient safety are improved, but measurement precision of plaque composition is worsened
Solution Approach 1:
The patent performs preliminary computational processing of non-invasive CT imaging data before the actual intervention procedure. By pre-processing the images to extract plaque composition information and generate mechanical models in advance, the system enhances the effective measurement precision available during the procedure without requiring more precise imaging hardware, thus maintaining ease of operation while improving data quality.
Solution Approach 2:
The patent transforms the raw CT imaging data by applying computational algorithms that extract and enhance plaque composition parameters. Through parameter transformation and computational enhancement, the system derives detailed plaque characterization (calcium score, lipid content, fibrous tissue) from standard non-invasive CT scans, effectively improving measurement precision without changing the imaging modality or increasing procedural complexity.
3Reliability
If multiple imaging modalities and computational models are integrated for intervention planning, then reliability of intervention outcomes is improved, but device complexity increases
Solution Approach 1:
The patent merges non-invasive CT imaging, mechanical modeling, and virtual intervention simulation into a single integrated computational platform. By combining these previously separate systems into one unified software platform that processes imaging data and generates intervention predictions simultaneously, the system improves reliability through multi-modal data integration while managing complexity through architectural consolidation rather than proliferation of separate devices.
Data Source
AI summary
A method and system are provided for planning a medical intervention, such as a coronary intervention. At least one image is retrieved, where the image includes at least a portion of a coronary artery. Based on the at least one image, a position and composition of plaque in the coronary artery are determined. A mechanical model of the portion of the coronary artery and the plaque in the coronary artery is generated, and a plurality of potential interventions is simulated in the context of the mechanical model. Following such simulations, an intervention for implementation is selected from the plurality of potential interventions.