Spectral Texture Analysis Map for CT Data
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Solution Overview
Problem
Conventional texture analysis techniques for spectral CT data fail to analyze organ and tissue textures effectively, as they do not inherently account for full spectral information and typically produce texture maps with lower spatial resolution compared to the original processed data.
Innovation Solution
A method and system for generating a texture analysis map from spectral image data using a spectral data texture processor that optimizes material-density and material-type textures by employing multi-dimensional spectral diagrams and co-occurrence matrix histograms, refining the texture analysis through iterations to achieve higher resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional texture analysis techniques are used on spectral CT data, then the analysis can be performed, but the full spectral information is not utilized and spatial resolution is reduced
Solution Approach 1:
The patent transitions from conventional 2D texture analysis to 4D spectral texture analysis by incorporating two additional dimensions: effective energy (keV) and attenuation coefficient. This dimensional expansion allows the system to utilize full spectral information from spectral CT data while maintaining high spatial resolution through sophisticated processing algorithms that analyze co-occurrence patterns across all four dimensions simultaneously
2Manufacturing precision
If conventional texture analysis methods are applied to spectral CT data, then texture maps can be generated, but the spatial resolution of the texture maps is lower than the original data
Solution Approach 1:
The patent segments the complex 4D spectral texture analysis into distinct processing stages: (1) generating multiple spectral images at different effective energies, (2) calculating attenuation coefficients for each pixel, (3) computing co-occurrence matrices for each energy level, (4) integrating results across all energy levels. This segmentation allows high-resolution texture analysis while managing computational complexity through modular processing
Solution Approach 2:
By adding the energy dimension to traditional spatial texture analysis, the patent creates a 4D analysis framework that preserves spatial resolution through advanced algorithms that process spectral information without requiring spatial downsampling, thereby maintaining manufacturing precision while handling increased device complexity
3Quantity of substance
If spectral CT imaging is performed, then spectral characteristics are captured, but conventional texture analysis cannot effectively analyze organ and tissue textures
Solution Approach 1:
The patent creates a universal 4D spectral texture analysis framework that can process multiple types of spectral CT data (different energy levels, different contrast agents, different tissue types) through a single integrated methodology. The system calculates co-occurrence matrices that adapt to various tissue characteristics while maintaining consistent analytical principles across all applications
Solution Approach 2:
The introduction of spectral dimensions (effective energy and attenuation coefficient) transforms the analysis framework from specialized 2D texture analysis to a universal 4D system that inherently adapts to different tissue types and pathological conditions by analyzing patterns across all spectral dimensions simultaneously
Data Source
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AI summary
A method includes obtaining at least a first energy dependent spectral image volume and a second different energy dependent spectral image volume from reconstructed spectral image data. The method further includes generating a multi-dimensional spectral diagram that maps, for each voxel, a value of the first energy dependent spectral image volume to a corresponding value of the second energy dependent spectral image volume. The method further includes generating a set of spectral texture analysis weights from the multi-dimensional spectral diagram. The method further includes retrieving a set of texture analysis functions, which are generated as a function of voxel intensity and voxel gradient value from a co-occurrence matrix histogram. The method further includes generating a texture analysis map through a texture analysis of the reconstructed spectral image data with the set of texture analysis functions and the set of spectral texture analysis weights and visually presenting the texture analysis map.