Endoscopic Route Guidance With Image-to-Body Divergence Compensation
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Solution Overview
Problem
Endoscopic procedures face challenges due to image-to-body divergence, where the actual anatomy during a procedure differs from the planned virtual 3D space, leading to inaccuracies in localizing lesions and performing biopsies, particularly in multi-organ cavities with dynamic shape changes.
Innovation Solution
A method and system that compensates for image-to-body divergence by deriving a procedure plan from two scans at different volumes, computing anatomical models, defining a region of interest, and providing guidance cues that adapt to the actual anatomy during the procedure, including alternate routes for impassable branches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a procedure plan is derived from a static CT image for endoscopic navigation, then the planning process is simplified and efficient, but the accuracy of lesion localization deteriorates due to anatomical changes between imaging and procedure
Solution Approach 1:
The system transitions from static CT-based planning to dynamic navigation by acquiring multiple images during the procedure and updating the anatomical model in real-time. This allows the system to adapt to anatomical changes while maintaining planning efficiency through automated image processing and transformation algorithms.
Solution Approach 2:
The system performs preliminary registration of multiple images at different volumes before the actual navigation procedure. By pre-computing transformation matrices and creating a multi-volume anatomical model in advance, the system prepares compensation data that will be applied during navigation to maintain localization accuracy despite anatomical changes.
2Productivity
If assisted endoscopy systems use virtual 3D cavity space for navigation guidance, then navigation speed and accuracy are improved, but the system reliability deteriorates due to CT-to-body divergence
Solution Approach 1:
The system implements feedback by continuously comparing the virtual 3D cavity space with actual endoscopic images acquired during the procedure. Discrepancies are detected and compensated for by updating the transformation matrices, ensuring that navigation guidance remains accurate despite anatomical changes.
Solution Approach 2:
The system changes parameters by computing transformation matrices that map points between different volumetric scans. These transformation parameters are applied to the virtual 3D model to adjust it dynamically, compensating for anatomical changes and maintaining navigation reliability.
3Measurement precision
If multiple scans at different volumes are used to compensate for image-to-body divergence, then localization accuracy is improved, but the system complexity increases
Solution Approach 1:
The system creates a virtual copy of the anatomical structure from multiple scans and applies mathematical transformations to this copy. This allows compensation for anatomical changes without requiring physical modification of the endoscopic system or additional hardware sensors.
Solution Approach 2:
The system replaces mechanical or physical compensation methods with computational approaches. By using image processing algorithms and mathematical transformations on multiple volumetric scans, the system achieves accuracy compensation without adding hardware complexity.
4Productivity
If the endoscope follows a preplanned route in virtual space, then navigation efficiency is improved, but the ability to adapt to actual anatomy deteriorates
Solution Approach 1:
The system performs preliminary planning in virtual space to establish an efficient navigation route. This preplanned route provides navigation efficiency while the system maintains the capability to adapt by comparing with actual images and adjusting the virtual model during the procedure.
Solution Approach 2:
The navigation system transitions from static preplanned routes to dynamic route adjustment. By continuously updating the virtual anatomical model with actual images, the system can modify the navigation route in real-time to adapt to actual anatomy while maintaining navigation efficiency through automated guidance.
Data Source
AI summary
A method for planning and guiding an endoscopic procedure, taking place in a hollow structure inside of a multi-organ cavity, that compensates for image-to-body divergence, first derives a procedure plan based on two scans of the multi-organ cavity at different volumes, which includes computing anatomical models for each scan, defining a region of interest (ROI) in the in the first scan, determining a route leading through the hollow structure to the ROI, deriving guidance cues that instruct how to maneuver the endoscope along the route toward the ROI, computing a 3D transformation that maps points in the first scan to corresponding points in the second scan, using the transformation to convert the route for the first scan into a corresponding route in the space of the second scan, and deriving guidance cues for the second scan that instruct how to maneuver the endoscope along the route toward the ROI.