Blood Vessel Grid Modeling for CFD-Free Parameter Estimation
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Solution Overview
Problem
Existing blood vessel parameter determination methods, particularly those relying on computational fluid dynamics (CFD) algorithms, are complex and require high grid quality and boundary conditions, which can hinder efficiency and accuracy in vascular disease diagnosis.
Innovation Solution
A method involving a trained model, such as a graph neural network, is used to determine blood vessel parameters by generating a grid model from an image, simplifying the process and reducing the need for high grid quality and boundary conditions, while incorporating CFD benefits through training with reference parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational fluid dynamics (CFD) algorithms are used to determine blood vessel parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing the blood vessel image to generate a grid model with nodes and edges before parameter determination. This pre-structuring of the data allows the subsequent parameter determination to be more efficient and accurate without requiring complex CFD algorithms during the actual measurement process.
Solution Approach 2:
The patent introduces an intermediary approach by using a grid model as a simplified representation of the blood vessel geometry. This grid model serves as an intermediary between the original medical image and the final parameter determination, enabling accurate measurements through a less complex computational pathway compared to direct CFD simulation.
2Measurement precision
If high grid quality and boundary conditions are required for CFD algorithms, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically generating the grid model and determining blood vessel parameters from the medical image without requiring manual intervention for boundary condition setup. The algorithm autonomously processes the image data, creates the necessary computational structure, and extracts parameters, eliminating the need for operators to manually configure complex boundary conditions.
Solution Approach 2:
The patent extracts only the essential geometric features from the blood vessel image to create a simplified grid model. By taking out only the necessary structural information (nodes and edges representing the vessel geometry), the system achieves accurate parameter determination without requiring the full complexity of the original image or manual boundary condition specification.
3Measurement precision
If complex CFD algorithms with high grid quality requirements are used, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent applies segmentation by dividing the blood vessel image into discrete grid elements (nodes and edges). This segmentation transforms the continuous medical image into a structured computational model that can be processed more efficiently. The segmented grid structure enables faster parameter calculation compared to traditional CFD methods while maintaining measurement precision.
Solution Approach 2:
The system replaces the mechanical/computational complexity of traditional CFD algorithms with a simplified mathematical approach based on grid model analysis. By substituting the complex fluid dynamics simulation with a streamlined calculation method operating on the pre-processed grid structure, the system achieves the same measurement precision with significantly improved processing speed and diagnostic efficiency.
Data Source
AI summary
A method for determining blood vessel parameters is provided. The method may include obtaining a blood vessel image of a target blood vessel. The method may also include generating a blood vessel model of the target blood vessel based on the blood vessel image. The blood vessel model is a grid model. The method may further include determining at least one blood vessel parameter of the target blood vessel based at least on the blood vessel model.