Vascularization Visualization via Connectivity-Based Path Analysis
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Solution Overview
Problem
Current methods for visualizing vascularization in medical imaging, such as CT, face challenges in accurately depicting the connectedness of blood vessels to tumors due to variability in vessel size and brightness, and are sensitive to segmentation algorithms and parameters, leading to difficulties in distinguishing relevant from irrelevant vascular structures.
Innovation Solution
A method and apparatus for evaluating spatially varying vascular connections between lesions and surrounding vasculature by generating image data that represents the strength of these connections, using techniques like prioritized region growing to identify strong vascular paths and analyzing weak connections, allowing for more robust visualization without relying on brightness thresholds or binary determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If segmentation techniques are used to identify blood vessels, then vascular structures can be visualized, but the results are sensitive to segmentation algorithms and parameters leading to inaccurate identification
Solution Approach 1:
The patent replaces traditional segmentation algorithms with a path finding approach that uses cost functions and connectivity analysis. Instead of relying on brightness thresholds and noise suppression parameters, the system evaluates vascular paths based on cost metrics that measure the strength of vascular connections, thereby eliminating sensitivity to segmentation parameter selection while maintaining accurate vascular identification
Solution Approach 2:
The patent changes the fundamental parameter used for vascular identification from brightness intensity (which requires thresholding) to vascular connection strength (measured by cost functions). This parameter transformation allows the system to identify vessels based on their functional connectivity to the lesion rather than their visual appearance, resolving the contradiction between accuracy and reliability
2Ease of operation
If brightness thresholding is used to identify vessels, then visualization is simplified, but smaller vessels with lower contrast become invisible
Solution Approach 1:
The patent substitutes brightness-based identification with connectivity-based path finding. The system uses cost functions to evaluate vascular paths and identifies vessels based on their connection strength to the lesion, not their brightness. This allows small, faint vessels to be detected through their functional connectivity rather than their visual intensity, maintaining simplicity while improving detection precision
Solution Approach 2:
The patent introduces cost functions and path evaluation metrics as intermediaries between the image data and vessel identification. Instead of directly thresholding brightness values, the system uses these intermediary calculations to assess vascular connection strength, enabling detection of low-contrast vessels while maintaining operational simplicity through automated cost-based evaluation
3Quantity of substance
If all visible vessels are displayed, then complete vascular coverage is achieved, but vessels with no connection to the tumor create noise and reduce diagnostic value
Solution Approach 1:
The patent extracts only the vascular paths that have strong connectivity to the lesion using path finding algorithms and cost function evaluation. By calculating and selecting only those vessels with significant vascular connection strength, the system removes irrelevant vascular structures from the visualization, maintaining complete coverage of relevant vessels while eliminating diagnostic noise from unrelated vasculature
Solution Approach 2:
The patent segments the vascular network into connected and unconnected components based on path analysis. By evaluating the strength of vascular paths and separating vessels with strong lesion connections from those without, the system selectively displays only the diagnostically relevant vascular structures, preserving signal clarity while maintaining appropriate vascular coverage
Data Source
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Figure 3A~3C
AI summary
An apparatus produces image space data (35) indicative of the spatially varying strength of the vascular connections between locations in the image space and a lesion or other feature of interest. The data may be presented by way of a maximum intensity projection (MIP) display in which the brightness of the image represents the strength of the vascular connection.