XR Surgical Navigation Pose Chaining for Obstruction-Resistant Tracking
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Solution Overview
Problem
Existing navigation systems in surgery face challenges in tracking surgical instruments due to intermittent pauses caused by obstruction, affecting accuracy, robustness, and ergonomics.
Innovation Solution
The implementation of extended reality (XR) headsets equipped with tracking cameras that enhance tracking performance through pose chaining operations, allowing robust tracking of tools and objects across a wide range of motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single near infrared stereo camera setup is used for tracking, then the system structure is simple, but tracking robustness deteriorates due to obstructions by personnel or objects
Solution Approach 1:
The patent combines multiple camera systems (NIR stereo cameras and visible light cameras) into a unified tracking system. The NIR cameras track fiducial markers while visible light cameras capture surgical scenes, and their data are fused through coordinate transformation to achieve continuous, obstruction-resistant tracking of surgical instruments and anatomy.
Solution Approach 2:
The patent introduces visible light cameras as intermediary devices that capture surgical scenes and fiducial markers. These cameras serve as mediators between the NIR tracking system and the surgical environment, enabling tracking continuity when NIR cameras are obstructed by converting scene images to tracking data through coordinate transformations.
2Measurement precision
If multiple tracking cameras are used to improve tracking robustness, then tracking accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the tracking function across multiple specialized camera systems. NIR stereo cameras are dedicated to tracking fiducial markers with high precision, while visible light cameras are dedicated to capturing surgical scenes. This segmentation allows each subsystem to optimize for its specific function while maintaining overall tracking accuracy.
Solution Approach 2:
The visible light cameras serve multiple functions: capturing surgical scenes for visualization and detecting fiducial markers for tracking. This multi-functionality reduces the need for separate dedicated tracking cameras, thereby controlling system complexity while maintaining tracking precision through the dual-use of visible light camera data.
3Reliability
If tracking information from multiple sources is combined, then tracking robustness improves, but data processing complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors tracking data from multiple sources, compares their consistency, and dynamically adjusts weightings in the data fusion process. This feedback loop ensures robust tracking by automatically relying more on reliable data sources while filtering out obstructed or noisy measurements in real-time.
Solution Approach 2:
The patent dynamically changes processing parameters such as coordinate transformation matrices and data fusion weightings based on the current tracking conditions. When certain cameras are obstructed, the system adjusts the parameters to prioritize data from unobstructed cameras, thereby maintaining tracking robustness without requiring complex manual intervention.
Data Source
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AI summary
A surgical system includes a camera tracking system that determines a first pose transform between a first object coordinate system and the first tracking camera coordinate system based on first object tracking information from the first tracking camera which indicates pose of the first object. The camera tracking system determines a second pose transform between the first object coordinate system and the second tracking camera coordinate system based on first object tracking information from the second tracking camera indicating pose of the first object, and determines a third pose transform between a second object coordinate system and the second tracking camera coordinate system based on second object tracking information from the second tracking camera indicating pose of the second object. The camera tracking system determines a fourth pose transform between the second object coordinate system and the first tracking camera coordinate system based on combining the first, second, and third pose transforms.