Trabecular Node-Link Graph Recovery for Low-Resolution Skeletal Images
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Solution Overview
Problem
Current osteoporosis diagnosis methods rely on bone mineral density (BMD) measurements, which fail to accurately assess bone microstructure, leading to misdiagnosis and over-diagnosis due to low resolution and lack of bony tissue information, and existing high-resolution technologies face limitations in constructing training databases and require excessive calculations.
Innovation Solution
A node-link graph-based bone microstructure post-processing technology that reconstructs skeletal images by representing trabeculae as links and nodes, reinforcing connectivity, and adjusting node locations to minimize structural discontinuity, enabling accurate bone strength evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution skeletal images are obtained through radiation imaging equipment, then bone microstructure information can be captured, but radiation exposure increases and signal-to-noise ratio decreases
Solution Approach 1:
The patent applies preliminary action by performing bone microstructure post-processing on low-resolution skeletal images before final diagnosis. The node-link graph representation and connectivity recovery algorithms are applied in advance to enhance microstructure visibility without requiring high-resolution imaging, thereby avoiding the need for high radiation exposure while still enabling accurate bone strength evaluation
Solution Approach 2:
The patent creates a simplified copy of the bone microstructure using node-link graph representation. Instead of directly imaging at high resolution, the system creates a topological copy of the trabecular structure that captures essential connectivity information, allowing diagnosis without requiring the full detail of high-resolution imaging
2Measurement precision
If artificial neural network-based image high resolution technology is applied, then skeletal image resolution can be improved, but construction of training database becomes difficult
Solution Approach 1:
The patent introduces node-link graph representation as an intermediary between low-resolution imaging and bone strength evaluation. This intermediate topological representation simplifies the processing requirement, eliminating the need for complex neural network training databases while still enabling high-resolution-like diagnostic capability through graph-based connectivity analysis
Solution Approach 2:
The patent changes the parameter space from pixel-based image resolution to graph-based topological parameters (nodes, links, connectivity). This parameter transformation allows the system to work with low-resolution input data while producing high-accuracy bone strength evaluations, bypassing the need for high-resolution training databases
3Measurement precision
If phase optimization design-based image high resolution technology is applied, then high-resolution magnification can be achieved, but excessive calculations are required
Solution Approach 1:
The patent segments the bone microstructure into discrete topological elements (nodes and links) rather than attempting full pixel-level reconstruction. This segmentation approach reduces computational complexity by focusing only on critical connectivity information, achieving high-resolution diagnostic capability with minimal calculation resources
Solution Approach 2:
The patent extracts only the essential connectivity information from the bone microstructure using node-link graph representation. By taking out only the critical topological features needed for bone strength evaluation rather than reconstructing all image details, the system achieves high-resolution magnification效果 with significantly reduced computational burden
4Productivity
If BMD measurement method is used for bone strength evaluation, then diagnosis can be performed, but accuracy is limited to about 70%
Solution Approach 1:
The patent combines BMD measurement information with node-link graph-based microstructure connectivity analysis to create a composite evaluation system. This composite approach integrates both quantitative density data and qualitative structural connectivity data, achieving bone strength evaluation accuracy of about 94% while maintaining diagnostic efficiency
Data Source
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AI summary
Various embodiments relate to a computer apparatus for the bone microstructure connectivity recovery of a skeletal image reconstructed through an artificial neural network using the representations of a node-link graph-based bone microstructure and a method thereof. The computer apparatus and the method may be configured to represent a node-link graph from a bone microstructure of an input skeletal image, reinforce a connectivity of the bone microstructure in the node-link graph, and change the node-link graph into a skeletal image.