Robotic Catheter Collision Detection Using Virtual Representations
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Solution Overview
Problem
Current robotic catheter guidance systems lack effective collision detection and avoidance capabilities, which can lead to unintended collisions and tissue trauma during medical procedures, especially when multiple medical devices are used simultaneously.
Innovation Solution
The system employs an electronic control unit with collision detection logic that uses virtual representations of medical devices and anatomical structures to determine collision metrics and generate alternate paths, preventing collisions by assessing intersections of geometrical solids and employing an Oct-tree technique to reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collision detection logic continuously monitors multiple medical devices using detailed virtual representations, then collision detection precision is improved, but computational burden and processing time increase
Solution Approach 1:
The system segments the complex collision detection problem into hierarchical levels: (1) coarse-level bounding box or spherical envelope checks for rapid exclusion, (2) intermediate-level spline curve intersection tests for device centerlines, and (3) fine-level detailed surface geometry analysis only when lower levels indicate potential collision. This segmentation enables the system to maintain high detection precision while minimizing computational burden by applying detailed analysis only where necessary.
Solution Approach 2:
The system performs preliminary collision risk assessment using simplified geometric representations (bounding volumes, spherical envelopes) before committing to computationally intensive detailed mesh analysis. By pre-filtering device trajectories and identifying only those with potential collision risk, the system prepares data structures and selects appropriate detection algorithms in advance, significantly reducing real-time processing time while maintaining detection sensitivity.
2Measurement precision
If the system uses complex geometrical solid intersections for collision detection, then collision detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system applies different levels of geometric representation quality to different regions and contexts: simplified spherical or cylindrical envelopes for devices in safe zones, spline-based centerline representations for devices in proximity, and detailed mesh geometries only at potential collision points. This local quality approach ensures high detection accuracy where needed while reducing overall system complexity by using simpler models elsewhere.
Solution Approach 2:
The system dynamically adjusts the level of geometric complexity based on real-time device positions, velocities, and predicted trajectories. When devices are far apart or moving in clearly non-colliding paths, the system uses simplified geometric models. As devices approach potential collision zones, the system automatically increases geometric detail and detection resolution, optimizing the balance between accuracy and computational resources.
3Reliability
If the system monitors all medical devices simultaneously with high sensitivity, then collision detection reliability is improved, but false positive rate increases
Solution Approach 1:
The system implements multi-level feedback mechanisms: (1) immediate feedback from coarse geometric envelope intersections that trigger more detailed analysis, (2) feedback from spline intersection tests that refine collision probability assessment, and (3) feedback from detailed mesh analysis that confirms or refutes collision predictions. This hierarchical feedback system enables the system to maintain high sensitivity for reliable detection while using successive refinement to eliminate false positives before issuing alerts.
Solution Approach 2:
The system performs preliminary plausibility checks using device physical constraints (minimum separation distances, anatomical boundary limitations, device orientation constraints) before committing to full collision detection algorithms. By pre-filtering obviously impossible collision scenarios based on known physical and anatomical constraints, the system reduces false positive rates while maintaining detection sensitivity for genuine collision risks.
Data Source
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AI summary
A robotic catheter control system includes a collision detection logic configured to determine a collision metric indicative of a collision between a medical device that is manipulated by the robotic control system and an object. The object may be an anatomical feature or can be another medical device, including another device being manipulated by the robotic control system. The collision detection logic produces virtual representations of the medical device and the object and uses these representation to determine collision. Geometrical solids, such as spheres, are used to represent the outer surfaces of the devices and the logic determines whether the respective surfaces intersect, thereby indicating collision. Collision avoidance involves estimating future device poses and then computing an alternate path computation so as avoid predicted collision(s).