Vascular Data Association for Multimodal Imaging Integration
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Solution Overview
Problem
Current technologies lack a unified method to combine detailed data from various imaging methods for vascular structures, such as CT, X-ray, IVUS, and OCT, which hinders comprehensive visualization and analysis of vessel anatomy and surrounding matter.
Innovation Solution
A method is developed to associate geometric representations of vascular structures with intravascular data by identifying points of interest in both external and internal data sets, allowing for matching and combination of data from different acquisition methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data acquisition methods (CT, X-ray, IVUS, OCT) are used to obtain detailed vascular data, then measurement precision and information completeness are improved, but device complexity and data integration difficulty increase
Solution Approach 1:
The patent segments the vascular data into two distinct sets: external data (CT, X-ray) providing geometric representation and vessel centerline, and internal data (IVUS, OCT) providing matter composition information. By processing and matching these segmented data sets separately before integration, the system manages complexity while preserving measurement precision from both sources.
Solution Approach 2:
The patent introduces an intermediary matching process that uses identifiable features (calcified matter, side branches, lumen deviations) as mediators to align external and internal data sets. This intermediary matching layer enables integration of multiple acquisition methods without direct complex interaction between all data sources.
2Measurement precision
If intravascular data acquisition methods (IVUS, OCT) are used to identify matter in vessel walls, then measurement precision of matter composition is improved, but geometric information completeness deteriorates
Solution Approach 1:
The patent merges intravascular data (which provides matter composition but lacks geometric context) with extracorporeal data (which provides geometric representation but lacks matter details). The matching process combines these complementary data sets, allowing the final integrated model to contain both matter composition information and geometric information that neither source could provide alone.
3Loss of information
If external imaging methods (CT, X-ray) are used to obtain geometric representation, then geometric information completeness is improved, but matter composition detection capability deteriorates
Solution Approach 1:
The patent combines external imaging data (providing complete geometric representation including vessel centerline, bends, and overall structure) with internal imaging data (providing matter composition). The integration ensures that the final model retains the geometric completeness from external methods while adding matter composition details from internal methods.
4Loss of information
If data from multiple acquisition methods are combined to create unified model, then information completeness is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent performs preliminary actions by first extracting key identifiable features (calcified matter locations, side branch positions, lumen deviation points) from both external and internal data sets before attempting full integration. This preliminary feature extraction and matching simplifies the subsequent alignment process and makes the combination of multiple data sources more manageable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the creation of a unified data model that integrates geometric and matter-related data from diverse imaging sources, enhancing visualization and analysis of vascular structures and anomalies.
Implementation Method 1
The vessel lumen can be imaged for example using X-ray and contrast dye injected in the vessel
Implementation Method 2
using intravascular ultrasound (IVUS)
Implementation Method 3
using Optical Coherence Tomography (OCT)
Data Source
AI summary
There is no one-technology-fits-all data acquisition technology for body vessel, anomalies in vessel walls and per-vascular matter. Therefore, to be able to collect all acquirable data in one model or in one visualisation, data obtained using various data acquisition methods is to be combined. This is a challenge in particular if some data is collected from outside the body-CT or X-ray and other data is collected intravascular-IVUS or OCT; with IVUS and OCT, geometrical data like bends in vessels is not visible. By identifying points of interest related to particular features of a vessel, like side branches, data obtained using different acquisition methods may be aligned by associating points of interest in two or more data sets that identify the same feature over the data sets and using that as a basis for merging data to provide image data with vessel and matter around it.