Coronary 3D Vessel Reconstruction from Uncalibrated Angiography
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Solution Overview
Problem
Current 3D reconstruction methods for coronary arteries from angiography images are invasive, require substantial operator input, and lack accuracy in predicting Fractional Flow Reserve (FFR), especially due to image quality issues and computational modeling assumptions, making them unsuitable for widespread clinical deployment.
Innovation Solution
A multi-stage neural network model is trained using synthetic coronary vessel trees from MRA and CTA data to perform 3D reconstruction from uncalibrated 2D X-ray angiography images, achieving sub-pixel accuracy in vessel radius and reducing centerline reconstruction errors, without the need for image calibration or parameter correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional 3D reconstruction methods are used for coronary arteries from angiography images, then operator input and manual processing are required, but this increases device complexity and reduces productivity
Solution Approach 1:
The system performs self-service by automatically reconstructing 3D coronary artery models from 2D angiography images without requiring operator intervention. The neural network model independently processes the images, identifies vessel structures, and generates 3D reconstructions, making the system self-sufficient and highly automated.
Solution Approach 2:
The patent replaces manual mechanical operations with an automated neural network-based system. Instead of operators manually tracing and reconstructing vessels, a deep learning model automatically performs the reconstruction, substituting human mechanical work with intelligent automated processing.
2Measurement precision
If computational modeling assumptions are made to simplify FFR prediction, then processing speed increases, but measurement precision and reliability of FFR prediction deteriorate
Solution Approach 1:
The system performs preliminary action by pre-processing angiography images to extract accurate 3D coronary artery geometry and physiological parameters before FFR calculation. This preliminary extraction of precise anatomical data enables more accurate FFR predictions without requiring complex real-time computational assumptions during the actual FFR calculation phase.
Solution Approach 2:
The patent creates an accurate digital copy of the coronary artery geometry and physiological conditions from 2D angiography images. This virtual 3D model serves as a faithful replica that can be used for FFR simulation, allowing precise predictions to be made on the digital copy without repeatedly processing the original complex imaging data.
3Measurement precision
If image calibration and parameter correction are performed to improve reconstruction accuracy, then measurement precision improves, but loss of time and processing duration increase
Solution Approach 1:
The neural network model performs self-service by automatically correcting image parameters and calibration issues during the reconstruction process without requiring separate manual calibration steps. The model inherently handles perspective corrections, magnification adjustments, and geometric transformations as part of its automated processing pipeline.
Solution Approach 2:
The patent merges multiple calibration and correction operations into the single automated reconstruction process. Instead of performing separate calibration, correction, and reconstruction steps, the neural network integrates all these functions into one unified automated workflow, eliminating time losses between sequential operations.
4Measurement precision
If multiple angiography images from different projections are processed to improve 3D reconstruction accuracy, then measurement precision improves, but device complexity and processing requirements increase
Solution Approach 1:
The system performs self-service by automatically selecting, aligning, and processing multiple projection images without requiring operator intervention. The neural network independently determines which images to use, how to register them in 3D space, and how to integrate the information from different projections to achieve accurate reconstruction.
Solution Approach 2:
The patent implements a universal processing framework that can handle multiple projection angles and viewpoints through a single automated system. The neural network model is designed to process various types of angiography images from different projections using the same algorithmic approach, making the system versatile and reducing the need for specialized processing for each projection type.
Data Source
AI summary
A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.