Image-Based Branch Mapping for Accurate Luminal Navigation
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Solution Overview
Problem
Existing medical navigation systems struggle to accurately navigate medical instruments through complex luminal networks, such as bronchial or renal networks, due to challenges in identifying and mapping branch openings, which can lead to inefficiencies and complications during procedures like bronchoscopy and ureteroscopy.
Innovation Solution
The system employs image-based branch detection and mapping using an imaging device on the instrument to identify openings in the luminal network, comparing features of these openings to a preoperative model to provide an updated position state estimate, enhancing navigation by determining the probability of correct branch selection and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based branch detection and mapping is implemented, then navigation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary computational layer that processes images from the imaging device and matches them against a preoperative model to determine position state. This intermediary processing layer resolves the contradiction by providing accurate navigation through intelligent image-model registration rather than relying on complex hardware modifications to the instrument itself.
Solution Approach 2:
The preoperative model is created and prepared before the actual procedure. By performing the complex 3D modeling and branching structure preparation in advance, the system reduces real-time computational complexity during navigation while maintaining high accuracy through the pre-computed anatomical reference framework.
2Productivity
If real-time image processing and branch mapping is performed, then navigation efficiency is improved, but processing time increases
Solution Approach 1:
The system performs extensive preoperative imaging, segmentation, and model generation before the procedure begins. By completing the heavy computational workload of creating the 3D luminal network model and identifying all branch openings in advance, the system enables rapid real-time navigation without significant processing delays during the actual intervention.
Solution Approach 2:
The system creates a virtual copy (digital model) of the patient's luminal network from preoperative images. This digital twin allows the system to perform complex spatial reasoning and branch identification algorithms efficiently during navigation, as the model can be rapidly queried without processing actual anatomical structures in real-time.
3Reliability
If probabilistic position state estimation is provided, then navigation reliability is improved, but information processing complexity increases
Solution Approach 1:
The system continuously compares real-time instrument position data with the preoperative model to generate probabilistic position state estimates. This feedback mechanism provides reliability by quantifying confidence levels in position determination, allowing the system to identify when high precision is achievable versus when uncertainty increases, thereby managing information processing complexity adaptively.
Data Source
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AI summary
Navigation of an instrument within a luminal network can include image-based branch detection and mapping. Image-based branch detection can include identifying within an image one or more openings associated with one or more branches of a luminal network. Image-based branch mapping can include mapping the detected one or more openings to corresponding branches of the luminal network. Mapping may include comparing features of the openings to features of a set of expected openings. A position state estimate for the instrument can be determined from the mapped openings, which can facilitate navigation of the luminal network.