Personalized Coronary Flow Simulation for Non-Invasive FFR
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current non-invasive methods for assessing fractional flow reserve in coronary arteries are limited by inaccurate modeling of boundary conditions, particularly in accounting for individual patient variability and collateral blood flow, leading to unreliable estimates of hemodynamic significance in coronary lesions.
Innovation Solution
An apparatus and method that utilize volumetric image data to create a personalized hyperemic boundary condition model, adapting predefined parameters based on individual anatomical, morphological, and spectral features of the coronary tree to simulate blood flow more accurately, thereby determining a fractional flow reserve without the need for invasive measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If non-invasive computed tomography coronary angiography is used to assess fractional flow reserve, then patient risk and cost are reduced, but measurement precision and reliability of hemodynamic assessment deteriorate due to inaccurate boundary condition modeling
Solution Approach 1:
The patent applies preliminary action by extracting personalized features from volumetric image data before performing blood flow simulation. The system pre-processes anatomical, morphological, and spectral features from CT images to create patient-specific models, which are then used to personalize hyperemic boundary condition parameters before the actual FFR calculation, improving accuracy while maintaining non-invasive benefits
Solution Approach 2:
The patent implements parameter changes by transforming fixed, population-averaged boundary condition parameters into personalized parameters based on extracted image features. The system adjusts hyperemic boundary condition parameters (such as resistance and compliance) according to individual patient anatomy and tissue characteristics, enabling accurate non-invasive FFR measurement without invasive procedures
2Measurement precision
If personalized features are extracted from volumetric image data to adapt boundary condition models, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex analysis into distinct modules: volumetric image data acquisition, feature extraction (anatomical, morphological, spectral), boundary condition model adaptation, and FFR calculation. This modular approach manages system complexity by organizing the processing pipeline into separate, manageable components that can be implemented and validated independently
Solution Approach 2:
The patent uses an intermediary approach by introducing a boundary condition model as a mediator between the volumetric image data and the blood flow simulation. The model translates extracted image features into physiologically relevant parameters, bridging the gap between anatomical imaging and hemodynamic assessment without requiring direct complex coupling
3Measurement precision
If conventional invasive catheterization is used to measure blood flow characteristics, then measurement precision improves, but patient risk and cost increase
Solution Approach 1:
The patent applies copying by creating a virtual model of the patient's coronary anatomy and hemodynamics based on non-invasive CT imaging. Instead of directly measuring pressure and flow with invasive catheters, the system copies the relevant anatomical and physiological characteristics into a computational model that replicates the hemodynamic behavior, providing accurate FFR assessment without physical intrusion
Solution Approach 2:
The patent replaces the mechanical invasive measurement system with a computational approach. Instead of using physical catheters and pressure sensors that require vascular access, the system substitutes a blood flow simulation based on Navier-Stokes equations and personalized boundary conditions, eliminating the need for mechanical intrusion while maintaining measurement capability
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The present invention relates to an apparatus (26) and a method for determining a fractional flow reserve. For this purpose, a new personalized hyperemic boundary condition model is provided. The personalized hyperemic boundary condition model is used to condition a parametric model for a simulation of a blood flow in a coronary tree (34) of a human subject. As a basis for the personalized hyperemic boundary condition 5 model, a predefined hyperemic boundary condition model is used, which represents empirical derived hyperemic boundary condition parameters. However, these empirical hyperemic boundary condition parameters are not specific for a human subject under examination. In order to achieve a specification of the respective predefined hyperemic boundary condition model, specific human subject features are derived from a volumetric image of the coronary 10 tree of the human subject. These features are used to adjust the predefined hyperemic boundary condition model resulting in a personalized hyperemic boundary condition model. As an effect, a flow simulation using the parametric model conditioned by the personalized hyperemic boundary condition model improves the performance of flow simulation in order to determine an enhanced fractional flow reserve.15