Progressive Anatomical Registration for Branched Passageway Navigation
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Solution Overview
Problem
Existing minimally invasive medical techniques face challenges in accurately registering medical tools with anatomic passageways due to the likelihood of converging to false local minima during iterative registration, particularly in complex and deformable structures like branched passageways.
Innovation Solution
A progressive iterative point matching technique that initially registers more stable portions of the anatomic models and progressively expands to more complex areas, using noise introduction to reduce false minima, and employs a combination of rigid and non-rigid transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If iterative registration is used to align medical tools with anatomic images, then registration can be performed automatically, but the system may converge to false local minima reducing accuracy
Solution Approach 1:
The patent segments the anatomic structure into multiple subsets or regions, performing registration iteratively on each subset rather than attempting to register all points simultaneously. This segmentation approach breaks the complex global optimization problem into smaller local problems, reducing the likelihood of converging to false local minima and improving overall registration accuracy while maintaining automation.
2Adaptability or versatility
If registration is performed on complex and deformable structures like branched passageways, then the system can handle diverse anatomical variations, but the likelihood of converging to false local minima increases
Solution Approach 1:
The patent divides complex and deformable anatomic structures into multiple subsets, allowing the registration algorithm to process simpler local regions separately. This segmentation reduces the complexity of the optimization landscape in each subset, making it more reliable to converge to optimal solutions while still accommodating diverse anatomical variations across the entire structure.
Solution Approach 2:
The patent performs preliminary registration on stable, easily identifiable portions of the anatomic structure first, establishing a baseline alignment. This preliminary action creates a better initial configuration for subsequent registration of more complex and deformable regions, improving the reliability of convergence to optimal solutions by avoiding poor initial guesses that lead to false local minima.
3Productivity
If a single transformation is applied to align all points, then the process is simpler and faster, but accuracy decreases in complex anatomical regions
Solution Approach 1:
The patent segments the point sets into multiple subsets and determines transformations for each subset separately rather than applying a single global transformation. This approach maintains computational efficiency by processing smaller subsets independently while significantly improving alignment accuracy in complex anatomical regions where a single transformation would be insufficient.
Solution Approach 2:
The patent applies multiple partial transformations to different subsets of points rather than a single comprehensive transformation. This partial action approach allows each transformation to be optimized for its specific subset, achieving higher overall accuracy while maintaining reasonable computational speed by avoiding the need for a single complex global optimization.
Data Source
AI summary
A system receives a first set of points corresponding to an anatomical feature from a sensor(s) of a medical instrument. Each point in the first set of points represents a position in a first frame. The system receives a second set of points corresponding to the anatomical feature from an imaging system. Each point in the second set of points represents a position in a second frame. The system aligns a first subset of the first set of points with the second set of points based on a first transformation. The system transforms the first set of points based on the first transformation. The system aligns the first subset of the first set of points and a second subset of the first set of points with the second set of points based on a second transformation. The system transforms the first set of points based on the second transformation.