3D Organ Model Deformation Using Relative Point Matching
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Solution Overview
Problem
Existing methods for generating 3D models of organs from tomographic images face challenges in accurately reproducing the shape, particularly due to inconsistencies in the relative positional relationship between anatomical features like valve annuli, leading to distorted models.
Innovation Solution
A computer program and apparatus that sets points on a 3D model to match the relative positions of corresponding points in tomographic images, ensuring accurate alignment and deformation to maintain correct anatomical relationships, thereby preventing distortion in the 3D model generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If points are manually set in the template to match points on valve annulus in tomographic images, then the template can be deformed to match the heart shape, but the relative positional relationship between valve annuli becomes inconsistent in three dimensions causing distortion
Solution Approach 1:
The patent applies preliminary action by pre-defining reference positions and relative positional relationships in the template before deformation. The system calculates and stores the relative positions between multiple valve annuli in the template model, then uses these pre-calculated relationships to guide the deformation process, ensuring that the relative positional consistency is maintained throughout the matching process.
Solution Approach 2:
The patent introduces reference positions as intermediary elements that mediate between the template and the tomographic images. By establishing reference positions in both the template and the images, and using them to define relative positional relationships, the system creates a stable reference framework that prevents distortion during the matching and deformation process.
2Ease of operation
If manual point setting is used to match valve annulus positions, then the deformation process can be initiated, but the automated程度 is low and time-consuming
Solution Approach 1:
The patent applies self-service by enabling the system to automatically identify and set corresponding points between the template and tomographic images. The system uses image processing techniques to automatically detect valve annulus positions in the tomographic images, then automatically matches them with points in the template based on the pre-defined relative positional relationships, eliminating the need for manual point setting.
Solution Approach 2:
The patent changes the operational parameters from manual coordinate input to automated image-based detection. By transforming the point setting process from a manual operation requiring user input to an automated process based on image analysis and pattern recognition, the system significantly reduces the time required while maintaining accuracy.
3Manufacturing precision
If the template is deformed to match individual valve annulus positions, then local accuracy is improved, but the overall 3D shape accuracy deteriorates due to inconsistent relative positions
Solution Approach 1:
The patent applies segmentation by dividing the deformation process into multiple coordinated stages. Each valve annulus is matched separately using its relative positional relationship with reference positions, but all deformations are coordinated through the common reference framework. This segmented approach allows local accuracy for each anatomical feature while maintaining global shape consistency through the unified reference system.
Solution Approach 2:
The patent applies local quality by allowing different regions of the template to be deformed with different transformation parameters while maintaining consistency through reference positions. Each valve annulus region can be optimized for local accuracy based on its specific anatomical characteristics, while the reference position system ensures that these local deformations collectively produce an accurate overall shape.
Data Source
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AI summary
A computer sets a first point on a first part in a plurality of tomographic images of an organ. The computer determines a relative position of the first point with respect to reference positions of the first part and a second part in the tomographic images. The computer sets a second point in association with the first point, on the first part in a 3D model representing a structure of the organ, such that a relative position of the second point with respect to reference positions of the first part and the second part in the 3D model matches the relative position of the first point. Then, the computer deforms the 3D model such that, when the tomographic images and the 3D model are placed in the same coordinate system, the position of the second point matches that of the first point.