Patient-Specific Finite Element Models for Endovascular Treatment Planning
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Solution Overview
Problem
Current endovascular treatment methods for cerebral aneurysms face high recurrence rates and mortality risks due to inadequate simulation of patient-specific anatomical models and fluid dynamics, with existing simulation tools providing unreliable predictions.
Innovation Solution
A system and method utilizing patient-specific anatomical models, high-fidelity finite element medical device models, and computational fluid dynamics to simulate medical device deployment and hemodynamic outcomes, considering factors like coil packing density, coil shape, aneurysmal neck size, and parent vessel flow rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simplified medical device models and basic fluid dynamic simulations are used, then simulation speed and ease of operation are improved, but measurement precision and reliability of hemodynamic predictions deteriorate
Solution Approach 1:
Patient-specific anatomical models are constructed from medical imaging data before the simulation process begins. This preliminary modeling step enables subsequent high-fidelity simulations to accurately represent individual patient anatomy, improving prediction precision without requiring complex real-time adjustments during simulation execution.
Solution Approach 2:
High-fidelity finite element models of medical devices are created as detailed digital copies that replicate the actual device geometry, materials, and structural properties. These accurate copies enable realistic simulation of device deployment and hemodynamic interactions, achieving reliable predictions while maintaining computational efficiency through optimized mesh generation and solution algorithms.
2Reliability
If high-fidelity finite element models and comprehensive fluid dynamic simulations are used, then measurement precision and reliability are improved, but device complexity and computational resources required increase
Solution Approach 1:
The simulation system is divided into distinct modular components: patient-specific anatomical modeling module, finite element device modeling module, fluid dynamic simulation module, and results analysis module. Each module can be independently developed, validated, and optimized, reducing overall system complexity while maintaining high-fidelity capabilities through specialized algorithms in each segment.
Solution Approach 2:
The simulation employs adaptive parameter adjustment techniques where mesh density, time step size, and material property specifications are automatically optimized based on the specific clinical scenario and device characteristics. This allows the system to maintain high reliability across diverse cases without requiring manual configuration of complex simulation parameters by users.
3Measurement precision
If patient-specific anatomical models and detailed device models are constructed, then measurement precision is improved, but loss of time for model construction and simulation setup increases
Solution Approach 1:
Patient-specific anatomical models are automatically generated from standard medical imaging datasets (CT or MRI scans) using streamlined segmentation algorithms. This preliminary modeling is performed once per patient and can be stored for future simulations, eliminating repeated model construction time while maintaining high anatomical precision across multiple treatment planning scenarios.
Solution Approach 2:
The simulation system incorporates automated model validation and quality assurance features that self-check anatomical model accuracy, mesh quality, and simulation setup correctness without requiring extensive manual verification. This self-service capability reduces setup time by eliminating tedious manual checking steps while maintaining measurement precision through algorithmic validation.
Data Source
AI summary
Systems and methods provide a novel computational approach to planning the endovascular treatment of cardiovascular diseases. In particular, the invention simulates medical device deployment and hemodynamic outcomes using a virtual patient-specific anatomical model of the area to be treated, high-fidelity finite element medical device models and computational fluid dynamics (CFD). In an embodiment, the described approach investigates the effects of coil packing density, coil shape, aneurysmal neck size and parent vessel flow rate on aneurysmal hemodynamics. A processor may receive patient clinical data used to construct the relevant anatomical structure model. The processor may access medical device models constructed using finite element analysis and three dimensional beam analysis, and simulates the deployment of selected medical devices in the anatomical structure model. The selected medical device models and the anatomical structure model mesh, allowing the processor to simulate hemodynamic outcomes using computational fluid dynamics.