Compound Proton Number Set for Material Classification
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Solution Overview
Problem
Current radiological methods for material identification, particularly in security and customs applications, face challenges in accurately distinguishing between materials with similar atomic numbers and in handling complex compositions due to limitations in the Jackson-Hawkes method and difficulties in deconvoluting intensity data from overlapping objects.
Innovation Solution
The method involves calculating a Compound Proton Number Set using X-ray measurements at multiple energies, which includes higher-order polynomial equations for energy-dependent coefficients, allowing for the generation of a segmented image dataset based on Compound Proton Number and mass thickness, enabling more precise discrimination and identification of materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional radiological methods using single effective atomic number are used, then the device complexity is low, but the measurement precision for material identification deteriorates when materials have similar atomic numbers
Solution Approach 1:
The patent transitions from a single effective atomic number parameter to a multi-dimensional Compound Proton Number Set that includes multiple orders (first order, second order, third order, etc.). This dimensional expansion allows differentiation of materials with similar atomic numbers by capturing higher-order compositional information, directly resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The invention changes the fundamental parameter used for material identification from a single effective atomic number to a set of compound-specific parameters including multiple orders of weighted atomic numbers and mass thickness. This parameter transformation enables more precise material discrimination while maintaining manageable system complexity through systematic data processing.
2Measurement precision
If the Jackson-Hawkes method is used to estimate linear attenuation coefficient, then the device complexity remains manageable, but the measurement precision deteriorates for compounds with similar atomic numbers
Solution Approach 1:
The patent segments the attenuation coefficient analysis into multiple independent components: first order compound proton number, second order compound proton number, third order compound proton number, and mass thickness. Each segment corresponds to a specific polynomial order and can be calculated independently from spectroscopically resolved intensity data, enabling precise material discrimination without overwhelming computational complexity.
Solution Approach 2:
The invention extends the Jackson-Hawkes approach by incorporating higher-order polynomial terms (second order, third order, etc.) beyond the traditional single effective atomic number. This dimensional expansion creates a multi-parameter space that better distinguishes between compounds with similar atomic numbers while maintaining a systematic calculation framework.
3Measurement precision
If spectroscopic analysis at multiple energies is performed, then the measurement precision for material identification improves, but the loss of information from overlapping objects increases
Solution Approach 1:
The patent transforms the intensity data from multiple energy measurements into compound-specific parameters (multiple orders of weighted atomic numbers and mass thickness) that are less susceptible to overlapping object interference. This parameter transformation preserves essential material identification information while reducing the impact of superimposed objects, thereby maintaining measurement precision without complete information loss.
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 ability to accurately identify and classify materials by providing a more comprehensive assessment of elemental composition and thickness, improving discrimination of curved or irregularly shaped objects and overlapping materials, and allowing for more complex analyses.
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 processing an image dataset of radiation emergent from a test object after its irradiation by a suitable radiation source is described which comprises the steps of: generating an image dataset comprising a spatially resolved map of items of intensity data from the radiation emergent from the test object; further, resolving the intensity data items spectroscopically between at least two energy bands across the spectrum of the source; numerically processing the spectroscopically resolved intensity data items to determine a further spatially resolved dataset of data items representative of one or more orders of Compound Proton Number and/or effective mass thickness and/or a density; generating a segmented image dataset using the said dataset of data items representative of one or more orders of Compound Proton Number and/or effective mass thickness and/or a density. The method applied as part of a method for the radiological examination of an object and an apparatus for the same are also described.