FFR Estimation via CFD Boundary Conditions
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Solution Overview
Problem
Current methods for estimating fractional flow reserve (FFR) in coronary arteries are either costly and invasive, or non-invasive computational fluid dynamics simulations are time-consuming and require high-quality geometrical data, with poorly defined boundary conditions affecting accuracy.
Innovation Solution
A method using a data analyzer to classify and estimate FFR based on extracted features from image data, determining boundary conditions for computational fluid dynamics simulations, and providing a confidence interval for the estimated FFR, allowing for non-invasive, accurate, and fast FFR classification and estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pressure wire measurement is used to obtain FFR, then measurement precision is improved, but device complexity and patient risk increase due to invasive procedure
Solution Approach 1:
The patent replaces the mechanical pressure wire measurement system with a computational fluid dynamics simulation system. Instead of physically inserting a pressure wire into the coronary artery to measure pressure differential, the system uses CFD simulations based on CT scan data to computationally determine FFR values, thereby eliminating the invasive mechanical procedure while maintaining measurement capability
Solution Approach 2:
The patent creates a virtual copy of the coronary artery geometry from CT scan images and performs simulations on this digital replica. This virtual model allows FFR measurement without requiring physical intervention in the patient's vasculature, substituting the physical measurement system with a computational counterpart
2Object-affected harmful factors
If CFD simulation is used to estimate FFR non-invasively, then patient risk is reduced, but computation time increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing the CT scan data to extract coronary geometry and pre-defining boundary conditions before the actual FFR calculation. This preparation work is done automatically and efficiently, setting up the simulation framework in advance to reduce the time required for the actual computational step
Solution Approach 2:
The patent changes the approach to boundary conditions by using automated methods to define inlet and outlet conditions based on the extracted geometry and standard physiological assumptions. This parameter optimization reduces computation time while maintaining accuracy, allowing faster convergence of the CFD simulation
3Measurement precision
If high-quality geometrical data is required for CFD simulation, then measurement precision is improved, but ease of operation deteriorates due to significant manual editing
Solution Approach 1:
The patent implements self-service by enabling the system to automatically extract coronary artery geometry from CT scan images and automatically define boundary conditions without requiring manual intervention. The system performs self-correction and self-optimization of the geometric model, eliminating the need for operators to manually edit segmentation data while maintaining high measurement precision
Solution Approach 2:
The patent performs preliminary automated processing of the CT data to create a ready-to-simulate geometric model. By pre-processing the image data to extract and refine coronary geometry automatically before simulation, the system eliminates the need for subsequent manual editing, making the workflow easier to operate while preserving geometric accuracy
Data Source
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AI summary
As described herein, an unknown FFR is classified based on certain extracted features. In addition, an estimation of the unknown FFR can be determined based on certain extracted features. Furthermore, a confidence interval can be determined for the estimated FFR. In another instance, boundary conditions for determining an FFR via simulation are determined. The boundary conditions can be used to classify the unknown FFR.