3-D Vessel Surface Reconstruction via Centerline Registration
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Solution Overview
Problem
Current 2-D coronary angiography methods face limitations such as foreshortening and out-of-plane magnification errors, leading to inaccurate 3-D vessel surface reconstruction, especially with limited image data, which complicates the identification of vessel geometry and stenosis in PCI procedures.
Innovation Solution
A method for reconstructing 3-D vessel geometry using non-rigid registration and hierarchical surface reconstruction with affine or deformable transformations, sampling based on median radii, and Poisson surface reconstruction, which improves accuracy and detail without requiring full surface reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 2-D projection images are used for vessel reconstruction, then the reconstruction process is simple, but foreshortening and out-of-plane magnification errors occur leading to inaccurate vessel geometry
Solution Approach 1:
The patent transitions from 2-D projection images to 3-D vessel surface reconstruction by integrating multiple 2-D images from different angles and using centerline correspondence establishment to build a 3-D model, thereby eliminating foreshortening and magnification errors inherent in 2-D projections
2Measurement precision
If multiple 2-D images with different angles are acquired to reduce foreshortening errors, then vessel geometry accuracy improves, but the complexity of image acquisition and processing increases
Solution Approach 1:
The patent uses a unified 3-D vessel surface reconstruction framework that can process multiple 2-D images from different angles and configurations (bi-plane or mono-plane) through a common centerline-based approach, making the system versatile while managing complexity
3Loss of time
If a limited number of 2-D images are used for reconstruction, then the processing time is reduced, but the reconstruction quality becomes blurred and low resolution
Solution Approach 1:
The patent performs preliminary centerline extraction and correspondence establishment before surface reconstruction, which allows the system to efficiently process limited 2-D images by first establishing the 3-D spatial framework through centerline points, thereby maintaining reconstruction quality while reducing processing time
4Measurement precision
If 3-D tomographic reconstruction is performed to achieve accurate vessel surface reconstruction, then vessel geometry accuracy improves, but computational cost increases significantly
Solution Approach 1:
The patent extracts only the essential vessel centerline information from 2-D images and uses this extracted data to construct the 3-D vessel surface, rather than performing full 3-D tomographic reconstruction of the entire volume, thereby reducing computational cost while maintaining accuracy
5Use of energy by moving object
If elliptical or circular models are used for symbolic reconstruction, then the reconstruction process is computationally efficient, but accuracy is reduced due to lumen deformation and lesion
Solution Approach 1:
The patent uses centerline points extracted from actual 2-D vessel images to define the 3-D vessel surface geometry, allowing local variations in vessel shape, lumen deformation, and lesion characteristics to be captured accurately rather than forcing a uniform elliptical or circular model
Data Source
AI summary
Reconstructing 3-D vessel geometry of a vessel includes: receiving a plurality of 2-D rotational X-ray images of the vessel; extracting vessel centerline points for normal cross sections of each of the plurality of 2-D images; establishing a correspondence of the centerline points from a registration of the centerline points with a computed tomography (CT) 3-D centerline, the registration being an affine or deformable transformation; constructing a 3-D centerline vessel tree skeleton of the vessel from the centerline points of the 2-D images; constructing an initial 3-D vessel surface having a uniform radius normal to the 3-D centerline vessel tree skeleton; defining sample points based sampling on median radii to the 3-D centerline vessel tree skeleton of the initial 3-D vessel surface; and constructing a target 3-D vessel surface by deforming the initial vessel surface using the sample points to provide a reconstructed 3-D vessel geometry of the vessel.