Aneurysm Learning Data Augmentation via Hemodynamic Image Rearrangement
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Solution Overview
Problem
Current aneurysm simulation methods are time-consuming and complex, making it difficult to derive results efficiently, especially when dealing with varying blood vessel shapes and complexities, which hinders accurate treatment planning and prediction of aneurysm prognosis.
Innovation Solution
A method and system for augmenting aneurysm learning data by performing simulations, predicting the position of smallest thickness in an aneurysm, setting center and peripheral positions, extracting blood flow data, and generating artificial images by rearranging and combining blood flow data from these positions, allowing for the creation of multiple artificial images based on different data arrangements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex simulation models and detailed analysis processes are used to improve aneurysm prediction accuracy, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent pre-calculates and stores hemodynamic parameters (such as wall shear stress, oscillatory shear index, and blood flow velocity) at multiple predetermined positions (center and peripheral positions) before actual prediction is needed. These pre-computed results are stored in a database, allowing rapid retrieval and comparison during clinical decision-making without performing full simulations at the time of prediction
Solution Approach 2:
The patent divides the aneurysm structure into multiple discrete positions including a center position and multiple peripheral positions. Each position has specific hemodynamic parameters measured and stored separately. This segmentation allows the system to analyze specific critical locations independently, improving both accuracy at key positions and efficiency by avoiding full-field analysis
2Measurement precision
If detailed hemodynamic simulation analysis is performed to improve treatment planning accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the most critical hemodynamic parameters (wall shear stress, oscillatory shear index, blood flow velocity) at specific key positions (center and peripheral positions) rather than performing comprehensive analysis of all hemodynamic variables throughout the entire aneurysm volume. This extraction approach maintains diagnostic accuracy while significantly reducing computational complexity
Data Source
AI summary
The present invention relates to a method and system for augmenting aneurysm learning data for augmenting artificial images formed of various result values calculated from simulation results. The method of augmenting aneurysm learning data according to the present invention includes: performing a simulation using aneurysm data; predicting a position having a smallest thickness in an aneurysm based on a result of the simulation; setting a center position at the predicted position; setting a plurality of peripheral positions at different positions having a preset radius from the center position; extracting blood flow data according to a preset sampling period for a reference time at each of the center position and the plurality of peripheral positions; converting the extracted blood flow data into an image to generate a blood flow image; and generating a central image and a peripheral image in which a plurality of blood flow images according to the center position and the peripheral position are arranged in the order of the reference time; and generating different artificial images by changing an arrangement order of the central image and the peripheral image.