Personalized 4D Heart Model for Aortic Wall Mechanics
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Solution Overview
Problem
Current methods for diagnosing and treating cardiac disease lack accurate patient-specific models of heart and aortic anatomy, leading to ineffective early disease prediction and progression models, as they rely on generic data rather than personalized parameters.
Innovation Solution
A method and system for generating a personalized 4D anatomical model of the heart using volumetric image data, incorporating patient-specific geometry, material properties, and fluid boundary conditions, which enables the estimation of aortic wall material properties and simulation of hemodynamics and wall mechanics through Fluid Structure Interaction simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic data is used for disease prediction and progression models, then the modeling process is simple and fast, but the accuracy and effectiveness of the models are insufficient for individual patients
Solution Approach 1:
The system segments the complex task of personalized cardiac modeling into distinct modules: image acquisition module, 4D geometric model generation module, material property estimation module, hemodynamic simulation module, and disease progression prediction module. Each module handles a specific aspect of the modeling process, making the overall complex system manageable and implementable through coordinated subsystems
Solution Approach 2:
The system performs preliminary actions by pre-establishing patient-specific 4D geometric models and material property databases before actual disease prediction is needed. These pre-computed models serve as foundational inputs that can be rapidly utilized for various diagnostic and prognostic scenarios, reducing real-time computational burden
2Reliability
If patient-specific geometry and material properties are incorporated into hemodynamic analysis, then the accuracy of disease prediction improves, but the computational complexity and data requirements increase significantly
Solution Approach 1:
The system employs parameter changes by utilizing patient-specific material properties (such as aortic wall elasticity, density, and structural characteristics) as input parameters for hemodynamic simulations. These personalized parameters replace generic assumptions, enabling more accurate prediction of disease progression while the computational model adapts its complexity to match the available patient data
3Measurement precision
If comprehensive patient-specific material properties of the aortic wall are determined, then the accuracy of wall mechanics analysis improves, but the measurement and characterization difficulty increases
Solution Approach 1:
The system introduces an intermediary computational approach by using observable imaging data (from CT or MRI scans showing aortic geometry and deformation) as intermediate measurements. These observable features serve as proxies that enable indirect estimation of difficult-to-measure material properties through inverse modeling and parameter optimization techniques
Solution Approach 2:
The system creates simplified computational copies or representations of the complex aortic wall material properties. Instead of directly measuring all physical properties, the system generates equivalent homogeneous material parameters that reproduce the observed mechanical behavior in simulations, making the characterization process more feasible
Data Source
AI summary
A method and system for generating a patient specific anatomical heart model is disclosed. A sequence of volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. A multi-component patient specific 4D geometric model of the heart and aorta estimated from the sequence of volumetric cardiac imaging data. A patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model is generated. Patient specific material properties of the aortic wall are estimated using the 4D geometrical model and the 4D computational model. Fluid Structure Interaction (FSI) simulations are performed using the 4D computational model and estimated material properties of the aortic wall, and patient specific clinical parameters are extracted based on the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.