Virtual Reference Model for Dental Data Matching
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Solution Overview
Problem
Existing methods for matching data sets related to a craniofacial space from different input sources with varying spatial resolutions often result in misalignment due to interpolation errors, leading to inaccuracies in dental restorative procedures.
Innovation Solution
A method and system that identify pre-defined relationships between surface and volume coordinates from different data sets, allowing for precise matching and transformation of coordinate systems to generate a high-precision matched data set for virtual planning and production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from different input sources with varying spatial resolution are matched using traditional interpolation methods, then the matching process can be completed, but misalignment and inaccuracies occur due to interpolation errors
Solution Approach 1:
The patent introduces a virtual reference model as an intermediary that mediates between the first and second data sets. This reference model serves as a common coordinate framework that resolves the mismatch between different spatial resolutions without requiring direct interpolation between the two data sources, thereby eliminating interpolation errors and improving alignment accuracy.
Solution Approach 2:
The patent transforms the matching problem from direct spatial coordinate matching to a dimensional transformation problem. By introducing a virtual reference model with its own coordinate system, the solution moves from 3D spatial matching to a multi-dimensional coordinate transformation problem, allowing for precise alignment through mathematical transformation rather than interpolation.
2Productivity
If traditional interpolation methods are used to match data from sources with different spatial resolution, then the matching can be performed, but errors are introduced that affect the reliability of dental restorative procedures
Solution Approach 1:
The virtual reference model acts as an intermediary that enables efficient matching while maintaining high precision. Instead of directly interpolating between two different resolutions (which introduces errors), the reference model provides a common framework that allows both data sets to be transformed into it, ensuring both productivity and manufacturing precision.
Solution Approach 2:
The patent performs preliminary coordinate transformation of both data sets into the virtual reference model's coordinate system before final matching. This preliminary action eliminates the need for error-prone interpolation during the final matching stage, ensuring that production data generated from the matched data maintains high accuracy.
3Ease of operation
If fiducial markers are used for matching data sets, then the matching process can be simplified, but interpolation errors still occur when identifying markers from data with different spatial resolutions
Solution Approach 1:
The virtual reference model serves as an intermediary that simplifies marker identification while maintaining precision. Markers are first identified in their respective local coordinate systems, then transformed to the virtual reference model's coordinate system. This approach maintains the simplicity of fiducial marker usage while eliminating interpolation errors through accurate coordinate transformation.
Solution Approach 2:
The patent moves marker identification from direct spatial location (3D) to a multi-dimensional coordinate transformation problem. By transforming marker positions into the virtual reference model's coordinate system, the solution maintains ease of operation while improving measurement precision through mathematical transformation rather than interpolation.
Data Source
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AI summary
A method of facilitating matching of a first and a second set of data related to a region of interest in a craniofacial space is disclosed. A first set of data is generated using a first, non-material penetrating, 3D data generating device, and a surface object (421) is identified in the first set of data. Further, a first coordinate (425) is identified in a first relation to the surface object (421). The second set of data is generated by using a second, material penetrating, 3D data generating device. A volume object (423) is identified in the second set of data, and a second coordinate is identified based on the volume object. The first and second coordinate have a pre-defined relationship to each other allowing for improved matching of the first set of data and the second set of data to a matched set of data.