Spectral CT Fingerprinting via Multi-Dimensional Histograms
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Solution Overview
Problem
Conventional CT scanners lack the ability to effectively identify the presence or absence of specific materials, such as normal and diseased tissue, due to their inability to utilize spectral characteristics in projection data, which are integrated over the energy spectrum.
Innovation Solution
Generating spectral projection data across different energy ranges, constructing basis images, and creating a multi-dimensional histogram to visually present and compare with known tissue types, allowing for the identification of abnormalities based on material-specific clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional CT imaging is used to generate volumetric image data, then structural information and radiodensity are obtained, but spectral characteristics and material composition information are lost
Solution Approach 1:
The patent segments the integrated projection data by dividing the energy spectrum into multiple discrete energy bins. Each energy bin captures attenuation data at specific energy levels, allowing spectral information to be preserved separately rather than being integrated into a single value. This segmentation enables material decomposition while maintaining a relatively simple imaging system architecture.
2Measurement precision
If dual energy spectral imaging is used to generate basis images, then material discrimination capability is improved, but the ability to identify specific tissue types and abnormalities is insufficient
Solution Approach 1:
The patent extends the material characterization from 2D basis image space to N-dimensional histogram space by incorporating multiple energy bins. Each voxel is represented by an N-dimensional vector of attenuation values across different energy bins, creating a unique spectral fingerprint. This dimensional expansion enables not only material discrimination but also specific tissue type identification and abnormality detection by comparing against reference spectra.
3Loss of information
If spectral projection data is collected across multiple energy ranges, then material-specific information is preserved, but data processing complexity increases
Solution Approach 1:
The patent transforms the complex spectral data by changing the representation parameters from raw attenuation values at multiple energies to histogram frequencies in N-dimensional space. By converting continuous spectral data into discrete histogram bins and comparing frequency distributions, the system simplifies the processing complexity while preserving all spectral characteristics for accurate material identification.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate identification of tissue types and abnormalities by leveraging spectral characteristics, improving diagnostic capabilities and system calibration through material attenuation decomposition plots.
Implementation Method 1
The x-ray tube rotates around an examination region located between the x-ray tube and the one or more detectors and emits radiation that traverses the examination region and a subject and/or object disposed in the examination region
Implementation Method 2
basis images reflecting intrinsic properties of a material being imaged (e.g., the photoelectric effect (PE) and Compton scattering (CS) behavior of each component of the tissue)
Implementation Method 3
basis images reflecting intrinsic properties of a material being imaged (e.g., the photoelectric effect (PE) and Compton scattering (CS) behavior of each component of the tissue)
Data Source
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AI summary
A method includes generating spectral projection data of a subject, including at least first spectral projection data corresponding to a first energy range and second spectral projection data corresponding to a second energy range, wherein the first and the second energy range are different. The method further includes constructing an image from a combination of the spectral projection data, and constructing a set of basis images for the 5 energy ranges and from the spectral projection data. The method further includes constructing a multi-dimensional histogram from the set of basis images, wherein the multi- dimensional histogram includes at least two axes, a first corresponding to a first basis component and a second corresponding to a second basis component, and the multi- dimensional histogram includes a set of clusters, including one cluster for each material 10 represented in the spectral projection data. The method further includes visually presenting, concurrently, the image and the multi-dimensional histogram.