Patient-Specific Heart Model for Cardiac Therapy Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cardiac electrophysiology therapies, such as cardiac resynchronization therapy, are often time-consuming and have a high number of non-responders due to the lack of personalized and effective treatment planning and guidance.
Innovation Solution
A patient-specific unified heart model is created using ultrasound images, incorporating cardiac electrophysiology, mechanics, and hemodynamics, which is used to simulate therapy outcomes and guide interventional procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cardiac EP therapy planning is used, then the treatment process is simple, but the therapy effectiveness is low with high non-responder rates
Solution Approach 1:
The system performs preliminary computational modeling and virtual therapy testing before the actual intervention. A patient-specific heart model is created from imaging data, and multiple therapy scenarios are simulated in advance to predict outcomes and identify optimal pacing configurations, allowing clinicians to prepare personalized treatment plans without during-procedure trial-and-error
Solution Approach 2:
The system creates a virtual copy of the patient's heart through computational modeling. This digital twin includes anatomical structures, electrophysiological properties, and hemodynamic characteristics, allowing therapy planning and outcome prediction without physical intervention on the actual heart during the planning phase
2Measurement precision
If personalized computational modeling is implemented, then therapy planning precision is improved, but the processing time and computational resources increase
Solution Approach 1:
Patient-specific models are generated and validated before the intervention procedure. Imaging data is processed offline to create the computational model, and multiple therapy scenarios are tested in advance, so that during the actual procedure, pre-computed results guide the intervention without requiring real-time complex calculations
Solution Approach 2:
The modeling process is divided into distinct phases: data acquisition from imaging, model construction from imaging data, validation against measured data, and therapy simulation. This segmentation allows computationally intensive tasks to be performed when time is available, while simpler tasks are performed during the procedure
3Adaptability or versatility
If multiple therapy scenarios are simulated, then the selection of optimal therapy is improved, but the computational load and complexity increase
Solution Approach 1:
A single patient-specific computational model serves multiple functions: it represents anatomical structures, simulates electrophysiological responses, predicts hemodynamic outcomes, and evaluates different therapy configurations. This multi-functional model allows comprehensive therapy assessment without requiring separate systems for each type of analysis
4Manufacturing precision
If real-time guidance during procedure is provided, then the intervention accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The virtual heart model is overlaid with real-time imaging data during the procedure, creating a augmented reality guidance system. The pre-computed optimal lead positions are displayed on the virtual model, which is then registered with actual anatomical landmarks visible in real-time imaging, guiding precise lead placement without requiring complex real-time control algorithms
Data Source
AI summary
A processor acquires image data from a medical imaging system. The processor generates a first model from the image data. The processor generates a computational model which includes cardiac electrophysiology and cardiac mechanics estimated from the first model. The processor performs tests on the computational model to determine outcomes for therapies. The processor overlays the outcome on an interventional image. Using interventional imaging, the first heart model may be updated/overlaid during the therapy to visualize its effect on a patient's heart.