Cardiac Map Registration Using Medial-Axis Graphs After Patient Motion
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Solution Overview
Problem
Existing cardiac mapping techniques face challenges in registering anatomical maps accurately during invasive procedures due to patient movement, which can lead to inaccurate ablation procedures and require time-consuming manual registration to external images.
Innovation Solution
The method generates a medial axis graph (skeleton) of the cardiac chamber to automatically register anatomical maps before and after patient movement, using skeleton registration to combine these maps and compensate for motion, allowing for efficient and automatic alignment without the need for manual registration to medical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual registration methods are used to align anatomical maps, then physician expertise can guide the registration process, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The system performs automatic registration by detecting anatomical landmarks and computing transformation parameters without requiring manual physician intervention. The computer automatically identifies corresponding points between maps and calculates alignment transformations, making the system self-sufficient for the registration task.
Solution Approach 2:
The patent replaces manual mechanical registration methods with an automated computational system. Instead of physicians manually manipulating and aligning maps, the system uses computer algorithms to detect landmarks, calculate deviations, and apply transformations automatically.
2Ease of operation
If manual registration methods are used, then flexibility in handling complex anatomical variations is maintained, but registration accuracy decreases due to human error
Solution Approach 1:
The system autonomously detects anatomical landmarks and computes registration transformations without human intervention, eliminating human error while maintaining operational flexibility through automated adaptation to various anatomical configurations.
Solution Approach 2:
The system detects deviations between anatomical landmarks in different maps and uses this feedback information to automatically compute and apply correction transformations, ensuring accurate registration while maintaining flexibility for different anatomical variations.
3Productivity
If automatic registration is implemented, then procedural time is reduced and consistency is improved, but complexity of the system increases
Solution Approach 1:
The patent replaces simple manual alignment procedures with automated computational systems that use algorithms to detect landmarks, calculate deviations, and apply transformations. This substitution increases automation and speed while managing complexity through software-based solutions.
Solution Approach 2:
The system introduces computational algorithms as intermediaries between the anatomical maps and the registration process. These algorithms automatically detect landmarks and compute transformations, serving as a mediator that simplifies the overall process while enabling automatic registration.
4Adaptability or versatility
If multiple anatomical maps are registered manually, then each map can be individually adjusted, but the cumulative time and potential for errors increase
Solution Approach 1:
The system automatically registers multiple anatomical maps by detecting landmarks and computing transformations for each map without requiring manual intervention for each individual adjustment, maintaining adaptability while dramatically reducing total registration time.
Solution Approach 2:
The system performs continuous automatic registration of multiple maps in sequence, maintaining the useful action of registration without interruption by manual operations. Each map is registered automatically following the previous one, eliminating idle time between adjustments while preserving individual map adaptability.
Data Source
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AI summary
A method includes calculating a first medial-axis tree graph of a volume of an organ of a patient in a first computerized anatomical map of the volume, acquired at a first time. A second medial-axis tree graph is calculated, of a volume of the organ of the patient in a second computerized anatomical map of the volume, acquired at a second time that is different from the first time. A deviation is detected and estimated, between the first and second tree-graphs. Using the estimated deviation, the first and second medial-axis tree graphs are registered with one another. Using the registered first and second tree graphs, the first and second computerized anatomical maps are combined.