Multispectral X-ray Material Identification
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Solution Overview
Problem
Current X-ray attenuation coefficient methods, such as those by Jackson and Hawkes, are limited in accurately determining the composition of mixtures and do not provide effective discrimination between materials engineered to have similar properties, leading to inaccuracies in security and industrial applications where the detection of concealed or non-conforming items is crucial.
Innovation Solution
A method utilizing a Compound Proton Number Set, calculated from X-ray measurements at multiple energies, which involves resolving intensity data spectroscopically across multiple energy bands to identify the composition of objects by treating the material attenuation coefficient as a set of energy-dependent polynomial equations, allowing for the calculation of higher-order powers of atomic numbers and providing a more accurate material identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional X-ray attenuation coefficient methods are used, then the measurement process is simple, but the measurement precision of material composition is insufficient
Solution Approach 1:
The patent transitions from conventional single-energy or dual-energy X-ray methods to multi-energy spectral analysis, adding the dimension of energy-resolved measurement. By collecting X-ray attenuation data across multiple energy levels and analyzing the spectral characteristics, the system achieves superior material composition determination accuracy. The energy-dependent attenuation coefficients provide additional discriminatory information that enables precise identification of materials with similar physical properties.
Solution Approach 2:
The patent employs parameter changes by utilizing the energy-dependent nature of X-ray attenuation coefficients. Different materials exhibit characteristic attenuation patterns across the X-ray energy spectrum. By measuring attenuation at multiple energy levels and analyzing how the attenuation coefficient varies with energy, the system can distinguish between different material compositions. This energy-parameter variation provides the basis for identifying materials that would be indistinguishable using conventional fixed-energy methods.
2Reliability
If conventional attenuation methods are used, then the analysis is straightforward, but the ability to discriminate between materials with similar properties is insufficient
Solution Approach 1:
The patent adds the energy dimension to X-ray analysis by measuring attenuation coefficients across multiple energy levels. This spectral dimension provides additional discriminatory power, as different materials exhibit unique energy-dependent attenuation patterns. The multi-energy approach enables reliable discrimination between materials with similar physical properties by analyzing how their attenuation characteristics vary with photon energy, thereby improving both reliability and precision simultaneously.
3Measurement precision
If multi-energy spectral analysis is performed, then material identification accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing energy-dependent attenuation coefficient data for various materials before actual analysis. During measurement, the system compares acquired spectral data against these pre-computed reference values, significantly reducing processing time. This preparatory computation enables rapid material identification while maintaining the high accuracy benefits of multi-energy spectral analysis, effectively resolving the time-accuracy tradeoff.
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
This approach enhances the accuracy of material identification by providing a detailed Compound Proton Number Set, enabling the discrimination of complex materials and improving the detection of concealed or non-conforming items, thereby improving security and quality control processes.
Implementation Method 1
The three most important methods of interaction are; Compton Scattering, Photoelectric Effect, Pair production
Implementation Method 2
The three most important methods of interaction are; Compton Scattering, Photoelectric Effect, Pair production
Implementation Method 3
The three most important methods of interaction are; Compton Scattering, Photoelectric Effect, Pair production
Implementation Method 4
The Beer-Lambert law states that for a beam of photons of energy E with intensity I0 incident on a material with thickness, t (cm), the intensity that emerges is
Data Source
AI summary
A method for monitoring objects for example for facilitating the identification and/or authentication of objects comprises: in a first recording phase: irradiating an object with a suitable source of radiation, collecting intensity information about radiation emergent from the object, resolving the intensity information spectroscopically between at least two energy bands, and storing the resultant dataset as a reference dataset;and in a second verification phase: irradiating an object with a suitable source of radiation, collecting intensity information about radiation emergent from the object, resolving the intensity information spectroscopically between at least two energy bands, and using the resultant dataset as a test dataset; identifying the object and retrieving its corresponding reference dataset; comparing the test dataset and the reference dataset within predetermined tolerance limits, and: in the event that the reference dataset and the test dataset correspond within the predetermined tolerance limits, treating the object as verified or in the event that the reference dataset and the test dataset differ by more than the predetermined tolerance limits, in a third identification phase: numerically processing the resolved intensity information from the test dataset to derive therefrom a dataset of information characteristic of the composition of the object, and using this information to identify the composition of the object.