Multi-Energy X-Ray Mass Determination with Beam Hardening Correction
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Solution Overview
Problem
Existing radiographic inspection systems face challenges in accurately determining the mass of objects with varying compositions due to beam hardening effects and material variations, leading to errors in mass estimation from X-ray scan images, especially when using single-energy level scans.
Innovation Solution
A method employing multiple X-ray scan images at different energy levels, with an initial learning mode to establish mass correlation factors and beam hardening ratios, allowing the system to automatically adjust for changes in ingredient proportions, using a combination of radiation generators and energy-resolving detectors to correct for beam hardening and composition variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-energy level X-ray scans are used to determine object mass, then the system complexity is low and operation is simple, but mass determination accuracy deteriorates due to beam hardening effects and material composition variations
Solution Approach 1:
The X-ray spectrum is segmented into multiple energy levels (at least two different energy levels), and the object is scanned at each energy level separately. This segmentation allows the system to capture different attenuation characteristics of materials at different energies, enabling accurate mass determination while compensating for beam hardening effects through multi-energy comparison.
Solution Approach 2:
The system changes the energy parameter of the X-ray radiation by operating the X-ray tube at multiple different energy levels (e.g., different kV settings). This parameter change enables the acquisition of attenuation data at different energy states, which when processed together, provide accurate mass information while correcting for composition variations and beam hardening effects.
2Measurement precision
If multiple energy level scans are implemented, then mass determination accuracy improves by accounting for composition variations, but the scanning time and productivity decrease
Solution Approach 1:
The system performs preliminary scanning at multiple energy levels to collect attenuation data before final mass calculation. This preliminary multi-energy scanning establishes the baseline attenuation characteristics that are then used to compute mass with high accuracy, allowing the process to be optimized for speed while maintaining precision.
Solution Approach 2:
The X-ray tube operates periodically at different energy levels in a cyclic manner, switching between energy states during the scanning process. This periodic energy switching enables multi-energy data acquisition within a single scanning cycle, improving productivity by reducing the need for separate scanning passes while maintaining mass determination accuracy.
3Measurement precision
If beam hardening effects are not corrected, then the measurement process is simple and fast, but mass estimation errors increase due to material composition variations
Solution Approach 1:
The system uses feedback from multi-energy attenuation measurements to detect and correct beam hardening effects. By comparing attenuation ratios at different energy levels, the system identifies beam hardening artifacts and applies corrective calculations to the mass determination process, thereby improving accuracy while managing the complexity through algorithmic correction.
Solution Approach 2:
The attenuation ratio between different energy levels serves as an intermediary parameter that mediates the relationship between raw X-ray measurements and final mass calculation. This intermediary ratio provides information about material composition and beam hardening effects, allowing the system to correct for these factors without requiring complex direct measurements, thus improving accuracy while controlling measurement complexity.
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 multi-energy method significantly reduces mass determination errors by accurately accounting for changes in composition, providing precise mass calculations and content percentage analysis, outperforming single-energy systems by maintaining low error rates across varying ingredient concentrations.
Implementation Method 1
imaging radiation originates as a fan-shaped planar bundle of rays from a localized source, e.g. an X-ray tube
Implementation Method 2
the absorption coefficient μ depends to a significant degree on the energy level of the radiation, with the lower-energy components of the spectrum typically being absorbed more, while the higher-energy components of the spectrum are typically absorbed to a lesser degree
Implementation Method 3
a linear array of photodiodes that are collectively referred to as a detector, wherein the fan-shaped radiation bundle and the linear array of photodiodes lie in a common plane
Implementation Method 4
a linear array of photodiodes that are collectively referred to as a detector
Implementation Method 5
the brightness or intensity I i (μ,d) of an individual pixel i as a function of the attenuation μ and the distance d travelled by the respective ray inside the material of the scanned object can be expressed by the exponential relationship
Data Source
Figure 1A~1B
Figure 2
Figure 3A~3B
AI summary
A method of determining the mass of an object by scanning the object in a radiographic inspection system, specifically in an X-ray scanner with the capability to take scan images at a plural number J of energy levels, has three modes or parts, namely: - an initial learning mode in which reference objects of a nominally identical material composition which varies within a range expected for the objects to be weighed are scanned and their actual masses are determined by weighing, whereupon the scanning system learns and stores an initial set of reference values for the reference objects as well as coefficient values through which the mass of a sample object can be determined from its scanned image; - a normal operating mode, wherein sample objects of unknown mass m are scanned and the scan image data are analyzed for compatibility with the reference values and, if found compatible, the sample object mass is determined from the scan data with the help of the learned coefficient values, and if the data are found incompatible, the method switches to the learning improvement mode; - a learning improvement mode in which the actual mass of the sample object whose scan image data were found incompatible is determined by weighing, whereupon the scanning system updates the stored reference values and coefficient values based on said scanned image data and said weighed mass.