Vasculature Modeling Near Real-Time Hemodynamics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing patient-specific 3D model simulations of cardiovascular systems are computationally expensive and impractical for real-time implementation, often producing inaccurate results due to numerical instabilities from unbalanced boundary conditions and inability to simulate hemodynamics under non-rest conditions, such as exercise or therapeutic effects.
Innovation Solution
A system and method that generate a reduced order model of a large artery using coherent boundary conditions from 3D data, coupled with a 0D model of the peripheral vasculature, allowing for near real-time simulations and personalization based on measured flow data, enabling predictions of hemodynamic changes from various parameters, including exercise or therapeutic substances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient-specific 3D model simulations are used to provide accurate hemodynamic data, then diagnostic precision is improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The cardiovascular system is divided into a large artery segment (modeled with 3D reduced-order model) and a peripheral vasculature (modeled with 0D model). This segmentation allows the computationally intensive 3D modeling to be applied only to the most critical segment while using faster 0D models for the remainder, achieving accurate local hemodynamics without full-system computational cost.
Solution Approach 2:
The patent transforms the full 3D simulation problem into a reduced-order model by changing the mathematical parameters from complete Navier-Stokes equations to simplified wave transport equations. This parameter change maintains accuracy for the large artery segment while dramatically reducing computational requirements, enabling near real-time simulation.
2Manufacturing precision
If traditional 3D models are used for large artery simulation, then local pressure and velocity fields are accurately described, but numerical instabilities occur due to unbalanced boundary conditions
Solution Approach 1:
A 0D model of the peripheral vasculature is introduced as an intermediary between the large artery segment and the rest of the circulatory system. This intermediary provides physiologically realistic boundary conditions that balance the 3D model, preventing numerical instabilities while maintaining accurate local pressure field descriptions.
Solution Approach 2:
The boundary conditions are made dynamic and adaptive rather than static. The 0D model continuously adjusts boundary conditions based on simulated flow and pressure, ensuring they remain balanced and physiologically accurate throughout the simulation, thereby preventing numerical instabilities.
3Ease of manufacture
If hemodynamic data is collected during patient rest state, then measurement is simplified, but the model cannot simulate hemodynamics under exercise or therapeutic conditions
Solution Approach 1:
Patient-specific geometric and physiological parameters are measured during the rest state when imaging is simple and non-invasive. These preliminary measurements are then used to personalize the model, which can subsequently simulate various conditions (exercise, therapeutic effects) by adjusting boundary conditions and parameters without requiring additional invasive measurements.
Solution Approach 2:
The model is designed to be universal and multi-functional. A single model framework can simulate multiple physiological states (rest, exercise, therapeutic conditions) by changing boundary conditions and input parameters, making the rest-state measurement sufficient for all simulation scenarios.
4Loss of information
If existing models are used for cardiovascular simulation, then some diagnostic information is provided, but they are impractical for real-time clinical implementation
Solution Approach 1:
The computational model is segmented into a detailed 3D reduced-order model for the large artery (providing accurate local hemodynamics) and a simplified 0D model for the peripheral vasculature (enabling fast computation). This segmentation maintains essential diagnostic information while achieving near real-time performance suitable for clinical workflows.
Solution Approach 2:
The model changes the mathematical parameters from full 3D Navier-Stokes equations to a hybrid reduced-order approach, maintaining key hemodynamic parameters (pressure, flow, wall shear stress) while reducing computational complexity to enable real-time clinical implementation.
Data Source
AI summary
A system and method of modeling flow of a vasculature in near real-time, is described. A vessel segment of the vasculature is modeled by a reduced order model, and a remainder of the vasculature is modeled by a 0D model. The reduced order model is generated using boundary conditions generated by a 0D model of the entire vasculature. Moreover, the reduced order model of the vessel segment and the 0D model of the remainder of the vasculature can be coupled to simulate flow that can be compared to actual flow measurements to personalize the 0D model of the remainder of the vasculature for a patient. Accordingly, a physician can update parameters of the personalized vascular system model to predict the effects of treatment protocols, including exercise or therapeutic substances, on the patient. Other embodiments are also described and claimed.