Vessel Tree Modeling via Minimum-Cost Path Extraction
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Solution Overview
Problem
Accurate and timely segmentation and modeling of blood vessels from contrast-enhanced Cardiac Computed Tomography Angiography (CTA)/Cardiac Magnetic Resonance Angiography (MRA) images are challenging due to intensity changes, vessel proximity to bright structures, significant scale variations, and pathological conditions like stenosis, leading to inadequate surface reconstruction, especially for small vessels like coronaries.
Innovation Solution
A method involving a vesselness measure computation using intensity profiles along rays, minimum-cost path detection in a discrete graph with vesselness-filtered edge costs, and iterative front propagation to extract centerlines and surface models, ensuring accurate representation of vascular structures without excessive user interaction or computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scale information from simple models is used for surface reconstruction, then computation is faster, but accuracy is insufficient especially for small vessels
Solution Approach 1:
The patent segments the vessel modeling process into two distinct phases: centerline extraction using simple scale information for speed, and surface reconstruction using intensive cross-sectional analysis for accuracy. This segmentation allows each phase to use the appropriate level of computational complexity.
Solution Approach 2:
The patent applies different quality levels to different parts of the modeling process. Cross-sectional analysis is performed with high precision at specific locations along the centerline to capture local vessel geometry accurately, while using simpler global scaling for the overall structure.
2Measurement precision
If complex deformable model techniques are used for vessel segmentation, then accuracy improves, but computation time increases and complexity increases
Solution Approach 1:
The patent divides the segmentation task into centerline detection and cross-sectional analysis. The centerline is extracted using efficient minimal path algorithms, while cross-sectional details are captured separately through intensity profile analysis, avoiding the need for complex global deformable models.
Solution Approach 2:
The patent introduces cross-sectional intensity profiles as an intermediary representation between the simplified centerline model and the full 3D vessel structure. This intermediary captures local geometric details without requiring complex deformable modeling of the entire vessel tree.
3Measurement precision
If binary vessel masks are created by traditional segmentation algorithms, then the process is simple, but accuracy is insufficient for quantifying vascular pathologies
Solution Approach 1:
The patent segments the vessel representation into centerline (1D), cross-sectional profiles (2D), and surface models (3D). This multi-level segmentation allows accurate pathology quantification at the cross-sectional level while maintaining computational efficiency at the centerline level.
Solution Approach 2:
The patent transitions from 2D binary masks to a multi-dimensional representation system where cross-sectional intensity profiles provide detailed local information. This dimensional expansion enables accurate pathology measurement without requiring complex 3D segmentation of the entire vessel tree.
Data Source
AI summary
A method for extracting a local center-axis representation of a vessel, includes: placing first and second seed points in an image that includes the vessel, wherein the first and second seed points are placed near a beginning and an end of a centerline of the vessel; representing the image as a discrete graph having nodes and edges, wherein the first seed point is a source node and the second seed point is a goal node; and finding a minimum-cost path between the first and second seed points by computing a cost of edges between the first and second seed points, wherein the cost of each edge is reciprocal to a vesselness measure of the edge.