Virtual Camera Navigation in 3D Lumen Data Sets
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Solution Overview
Problem
Existing methods for navigating a virtual camera through a hollow tubular object in three-dimensional volume data sets often fail to provide a high-quality real-time experience due to difficulties in achieving wall avoidance and efficient computation, especially when dealing with complex bends and branches.
Innovation Solution
A method that calculates navigation segments by determining the average ray length for groups of rays cast from a start point, selecting the group with the longest average length to set the segment direction, which biases the navigation towards the centerline, thereby avoiding the object wall and improving the field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a path is calculated in advance using mathematical techniques for wall avoidance, then wall avoidance is improved, but the complexity of computation increases and real-time interaction is reduced
Solution Approach 1:
The navigation path is divided into multiple segments, where each segment is calculated independently using ray casting from the current camera position. This segmentation allows the system to compute only the necessary portion of the path in advance, reducing overall computational complexity while maintaining wall avoidance through local optimization at each segment.
Solution Approach 2:
The system performs preliminary ray casting calculations to determine the direction of navigation segments before the camera actually reaches those positions. This preliminary action allows the path to be pre-planned to some extent, improving wall avoidance, while the incremental nature of segment-by-segment calculation keeps computational burden manageable.
2Ease of operation
If the camera path is calculated incrementally with user interaction, then ease of operation is improved, but the speed of navigation and real-time viewing experience deteriorates
Solution Approach 1:
The system calculates multiple navigation segments in advance before the user needs them, performing preliminary ray casting and path determination. This allows the camera to move quickly along pre-computed segments, maintaining high navigation speed while still allowing user interaction to adjust the path as needed.
Solution Approach 2:
The navigation system dynamically adjusts between pre-computed segments and user-directed movements. The system can switch between automated path following and user-controlled navigation, optimizing the balance between speed and interaction based on real-time user input and camera position.
3Area of stationary object
If the camera navigates close to the object wall, then the field of view is improved, but the likelihood of camera collision with the wall increases
Solution Approach 1:
The ray casting process performs local analysis of the lumen geometry at each camera position, evaluating wall distances in different directions. This local quality assessment allows the system to identify safe navigation directions that maintain optimal distance from the wall, balancing field of view requirements with collision avoidance.
Solution Approach 2:
Ray casting acts as an intermediary between the camera position and the object wall, providing information about wall distances and orientations. This intermediary measurement system enables the navigation algorithm to make informed decisions about camera positioning, achieving both good field of view and safe distance from walls.
4Ease of operation
If manual control of camera movement is used, then ease of operation is improved, but the ability to navigate complex lumens efficiently deteriorates
Solution Approach 1:
The complex lumen navigation task is segmented into manageable sections, with the system automatically calculating optimal paths through complex geometries while leaving final direction choices to the user. This segmentation allows users to benefit from automated efficiency in technical sections while maintaining manual control for decision-making.
Solution Approach 2:
The system performs self-service by automatically calculating navigation segments and suggesting optimal paths through complex lumen sections. This reduces the cognitive load on the user and improves navigation efficiency through automated pathfinding, while the user retains ultimate control to override or adjust the automated suggestions.
Data Source
AI summary
A method of navigating along a biological object with a lumen represented by a three-dimensional volume data set comprises generating a plurality of navigation segments connectable in a sequence, each segment having a start point within the lumen, a direction and a length. The navigation may be used for a camera in a virtual endoscopic examination, for example. The direction of each segment is determined by casting groups of rays outwards from the start point of the segment to the object wall, and calculating an average ray length for each group. The group having the largest average ray length is selected, and the axial direction of this group is used as the direction for the segment. The average ray lengths of the groups may be weighted using the direction of the previous segments to bias the navigation generally forward, or may be weighted using a view direction of the camera to allow a user to turn the camera into a chosen branch in the object.