Patient-Specific Cardiac Modeling for CRT Lead Placement
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Solution Overview
Problem
Current methods for guiding cardiac resynchronization therapy (CRT) lead placement are inadequate, as 30% of patients do not respond effectively, highlighting the need for improved patient selection and optimal lead placement guidance.
Innovation Solution
A patient-specific computational model of cardiac electro-mechanics is used to calculate and visualize cardiac parameters for various pacing locations and protocols, generating outcome maps that guide lead placement by overlaying results on interventional images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CRT lead placement methods are used, then the procedure is simple and quick, but the therapy effectiveness is low with 30% non-response rate
Solution Approach 1:
The system performs virtual pacing simulations and calculates outcome maps before the actual lead placement procedure. By pre-calculating the optimal lead positions and predicting therapeutic outcomes for different pacing locations, the system enables informed decision-making prior to the intervention, thereby improving therapy effectiveness while maintaining procedural efficiency.
Solution Approach 2:
The system creates a digital twin or computational model of the patient's heart that replicates its electrical and mechanical properties. This virtual copy allows for risk-free simulation of different lead placement scenarios and pacing protocols, enabling optimization of the treatment plan before actual implementation without exposing the patient to any risk.
2Measurement precision
If patient-specific computational modeling is performed, then lead placement accuracy is improved, but calculation time and computational resources increase
Solution Approach 1:
The system performs computationally intensive patient-specific modeling and outcome map generation before the interventional procedure. By completing these calculations in advance when computational resources are readily available and time is not critical, the optimized lead placement guidance is ready for rapid deployment during the actual procedure, thereby achieving high accuracy without time loss during the intervention.
Solution Approach 2:
The system employs adaptive computational strategies that adjust the level of model complexity and simulation detail based on the specific patient anatomy and clinical question. For routine cases, simplified models provide quick results, while complex cases can utilize more detailed models when time permits, thereby optimizing the balance between accuracy and computation time dynamically.
Data Source
AI summary
A system and method for visualization of cardiac changes under various pacing conditions for intervention planning and guidance is disclosed. A patient-specific anatomical heart model is generated based on medical image data of a patient. A patient-specific computational model of heart function is generated based on patient-specific anatomical heart model. A virtual intervention is performed at each of a plurality of positions on the patient-specific anatomical heart model using the patient-specific computational model of heart function to calculate one or more cardiac parameters resulting from the virtual intervention performed at each of the plurality of positions. One or more outcome maps are generated visualizing, at each of the plurality of positions on the patient-specific anatomical heart model, optimal values for the one or more cardiac parameters resulting from the virtual intervention performed at the that position on the patient-specific anatomical heart model.