3D Bone Tunnel Model Generation for Surgical Planning
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Solution Overview
Problem
Current techniques for characterizing existing bone tunnels in revision ACL reconstruction are inefficient and inaccurate due to the need for multiple software packages, subjective data selection, and manual manipulation of images.
Innovation Solution
A processor-implemented method that receives a 3D point cloud representing a bone tunnel, processes it to determine the first principal axis, endpoints, and projects points onto a 2D plane to generate a 3D object model, which is then provided to a surgical planning application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple software packages are used for characterizing bone tunnels, then measurement capability is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent combines multiple software functions into a single integrated system that performs tunnel detection, characterization, and surgical planning. The system integrates MRI/CT image processing, tunnel segmentation, 3D model generation, and surgical planning capabilities into one unified platform, eliminating the need for multiple separate software packages and reducing complexity.
Solution Approach 2:
The surgical planning system is designed as a universal platform that can handle multiple functions: importing various image formats (MRI, CT), detecting tunnels of different orientations, characterizing tunnel geometry (diameter, length, shape), generating 3D models, and providing surgical guidance. This multi-functional approach replaces the need for specialized separate software tools.
2Adaptability or versatility
If manual manipulation of images is performed, then flexibility in data processing is improved, but time consumption and productivity decrease
Solution Approach 1:
The system employs automated algorithms that perform tunnel detection and characterization without requiring manual image manipulation. The software automatically segments tunnels from MRI/CT images, calculates geometric parameters, and generates 3D models through self-service processing, eliminating time-consuming manual operations while maintaining flexibility through configurable processing parameters.
Solution Approach 2:
The patent replaces manual mechanical image manipulation with automated computational algorithms. Machine learning-based segmentation and automatic 3D reconstruction algorithms substitute for manual tracing and measurement techniques, dramatically improving productivity while maintaining adaptability through programmable processing pipelines.
3Ease of operation
If subjective data selection is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where the automated tunnel detection algorithm processes the complete set of imported images and provides results that can be reviewed and adjusted by the surgeon. The feedback loop allows verification of automatic segmentation accuracy and enables correction of any subjective selection biases, ensuring both ease of operation and measurement precision.
Data Source
AI summary
Some examples are directed to methods and related systems for generating 3D models of existing bone tunnels for surgical planning.