Deep Learning Vessel Analysis Using Synthetic Data
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Solution Overview
Problem
Current vascular analysis techniques face challenges in balancing computational intensity with accuracy, as three-dimensional anatomical modeling and fluid dynamics modeling require significant resources, while less complex approaches offer lower predictive benefits and accuracy.
Innovation Solution
The use of deep-learning methods trained with synthetic image data to perform tasks such as vessel segmentation, calcium removal, contrast level determination, and hemodynamic parameter estimation, which reduces the need for invasive data acquisition and enhances predictive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional anatomical modeling and fluid dynamics modeling techniques are used, then accuracy and predictive benefit are improved, but computational time and resources increase
Solution Approach 1:
The patent creates simplified two-dimensional cross-sectional copies of the three-dimensional vessel anatomy at specific locations. These 2D representations capture the essential geometric features needed for accuracy while avoiding the computational burden of full 3D modeling. The system generates multiple 2D cross-sections along the vessel length, which can be processed independently and efficiently.
Solution Approach 2:
The patent extracts only the critical geometric information needed for hemodynamic assessment from the complete 3D volume data. By taking cross-sectional slices at specific locations and extracting lumen boundaries and wall characteristics from these slices, the system obtains sufficient information for accurate prediction without processing the entire 3D dataset.
2Measurement precision
If three-dimensional anatomical modeling and fluid dynamics modeling techniques are used, then accuracy and predictive benefit are improved, but computational resources increase
Solution Approach 1:
The patent creates simplified two-dimensional cross-sectional copies of the three-dimensional vessel anatomy at specific locations. These 2D representations capture the essential geometric features needed for accuracy while avoiding the computational burden of full 3D modeling. The system generates multiple 2D cross-sections along the vessel length, which can be processed independently and efficiently.
Solution Approach 2:
The patent extracts only the critical geometric information needed for hemodynamic assessment from the complete 3D volume data. By taking cross-sectional slices at specific locations and extracting lumen boundaries and wall characteristics from these slices, the system obtains sufficient information for accurate prediction without processing the entire 3D dataset.
3Productivity
If less complex, dimensionally-reduced modeling approaches are used, then computational intensity is reduced, but accuracy and predictive benefit decrease
Solution Approach 1:
The patent applies different levels of modeling complexity to different locations along the vessel. By performing cross-sectional analysis at specific critical locations rather than continuous 3D modeling throughout the entire vessel, the system achieves computational efficiency while maintaining accuracy where it matters most. Each cross-section is analyzed with appropriate detail for its specific hemodynamic significance.
Solution Approach 2:
The patent transitions from three-dimensional continuous modeling to two-dimensional cross-sectional analysis at discrete locations. This dimensional reduction allows for much faster computation while the strategic selection of cross-section locations ensures that critical hemodynamic information is captured. The system can analyze multiple 2D slices independently and combine results for comprehensive assessment.
Data Source
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AI summary
The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.