3D Heart Electromechanical Simulation With Tissue-Specific Cell Models
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Solution Overview
Problem
Current computational models struggle to accurately simulate the complex interactions between electrophysiological and mechanical processes in the heart, particularly in modeling the beating heart and its pumping action, due to the high computational demands and inability to account for microscopic cellular behavior and population variability, which is crucial for clinical interventions and understanding cardiovascular diseases.
Innovation Solution
A computer-implemented method using the Finite Element Method (FEM) and Finite Differences Method (FDM) to create a three-dimensional heart model with electromechanical and electrophysiological aspects, incorporating a volume mesh with tissue types, cell models, and parallel computing to simulate the heart's function, including heterogeneous regions and medical interventions, and generate a virtual population model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed three-dimensional modeling with multiple tissue types and cell models is used to accurately simulate heart function, then simulation accuracy and physiological realism are improved, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The heart model is divided into multiple tissue types (e.g., myocardium, endocardium, epicardium) with distinct properties, and each tissue type is further segmented into functional elements with associated cell models. This segmentation allows accurate representation of physiological heterogeneity while enabling parallel computation across different tissue regions, thus managing computational complexity.
Solution Approach 2:
Different tissue types are assigned location-specific properties and cell models that reflect local physiological characteristics. For example, conduction velocity, action potential duration, and contractility parameters vary by tissue type and spatial location within the heart model, enabling physiologically accurate simulations without requiring uniform high-resolution modeling everywhere.
2Productivity
If parallel computing is used to reduce computational burden, then simulation time and resource requirements are reduced, but implementation complexity and programming difficulty increase
Solution Approach 1:
The computational domain is partitioned into multiple subdomains that can be processed in parallel. Each processing unit handles a specific tissue type or spatial region with its associated cell models, allowing simultaneous computation across the entire heart model. This segmentation naturally maps to parallel computing architectures while maintaining physiological accuracy.
3Reliability
If detailed cellular behavior and population variability are incorporated into the model, then physiological realism and clinical applicability are improved, but model complexity and data requirements increase
Solution Approach 1:
Cell models are assigned to functional elements based on local tissue type and spatial location, with parameters reflecting population variability. Different cell models (e.g., Purkinje cells, ventricular myocytes, atrial myocytes) are distributed throughout the model according to physiological distribution patterns, enabling realistic simulation of cellular heterogeneity without requiring explicit modeling of every individual cell.
Data Source
AI summary
A computer-generated, three-dimensional a heart model established with an electromechanical aspect and an electrophysiological aspect. The model may include tissue types and associated cell models comprising a system of ordinary differential equations describing properties for that tissue type, at least describing cell kinetics for ion channels of that tissue type. A three-dimensional model of a heart is constructed by creating a volume mesh comprising tissue type regions corresponding to each of the plurality of tissue types, wherein each tissue type region defines a local muscular fibre orientation in each tissue type region to correspond to a local alignment of muscular cells. The method further comprises running coupled three-dimensional electromechanical and electrophysiological simulations across the volume mesh. One or more derived physiological parameters are determined for the virtual patient.