Digital Dental Model Creation via Image Registration
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Solution Overview
Problem
Existing methods for creating digital denture models often suffer from distortions due to the curvature of the dentition and difficulties in accurately aligning and positioning teeth, particularly in areas that are hard to access, leading to inaccuracies in the representation of tooth surfaces and interdental spaces.
Innovation Solution
The method involves subdividing the position data of the reference model into unchangeable tooth-position-specific data and changeable surface-specific data, allowing for the adjustment of surface-specific data during image registration to align and correct the surface model with the reference model, while maintaining the integrity of the tooth section data and adjusting interdental spaces accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If image data is captured from hard-to-access areas of the dentition, then coverage of the tooth surface is improved, but distortion and positioning accuracy deteriorate
Solution Approach 1:
The dentition model is segmented into multiple tooth individual models, each captured and processed separately. This allows focused optimization of capture strategies for each tooth while maintaining overall dentition accuracy. The segmentation enables independent refinement of hard-to-access areas without compromising the entire model.
Solution Approach 2:
A coordinate system transformation process acts as an intermediary between the captured image data and the final dentition model. This intermediary layer allows for mathematical correction of distortions and precise positioning adjustments, bridging the gap between imperfect capture data and accurate final representation.
2Manufacturing precision
If the reference model is adjusted to match surface model details, then surface accuracy is improved, but tooth position relationships deteriorate
Solution Approach 1:
The reference model is segmented into tooth-position-specific components and surface-specific components. This segmentation allows independent processing: tooth position data maintains its original spatial relationships while surface data can be refined and adjusted for maximum accuracy without affecting positional integrity.
Solution Approach 2:
Different quality requirements are applied to different parts of the model. Tooth position data maintains high-fidelity spatial relationships, while surface data allows for localized refinement and adjustment. This local quality differentiation enables surface accuracy improvement without compromising overall positional accuracy.
3Manufacturing precision
If detailed surface data is captured for all tooth areas, then surface reproduction accuracy is improved, but data processing complexity and time deteriorate
Solution Approach 1:
The dentition is divided into individual tooth models, allowing selective processing priorities. High-detail capture is applied where needed, while maintaining efficiency through modular processing of each tooth segment independently, reducing overall computational burden.
Solution Approach 2:
Tooth individual models are created and prepared in advance as separate entities. This preliminary segmentation allows for optimized processing workflows where each tooth can be processed independently, enabling parallel computation and reducing total processing time while maintaining detailed surface accuracy.
Data Source
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AI summary
Method for creating a digital dental model (G), comprising the steps of digitally acquiring image data (DB) representing at least one surface section of a tooth (Z) or dentition (S), creating at least one surface model (O) from the image data (DB) and computationally merging the at least one surface model (O) with a reference model (R) to form the digital dental model (G), wherein the computational merging is carried out by means of image registration.