Multi-Energy X-Ray Base Material Decomposition Ghost Image Reduction
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Solution Overview
Problem
Current base material decomposition methods for multi-energy X-ray imaging, such as dual-energy sorting, face challenges in accurately selecting reference values for materials of unknown composition, leading to errors and 'ghost images due to subjective and time-consuming manual processes.
Innovation Solution
A method involving systematic selection of reference values for base materials through iterative processing of multi-energy X-ray images, minimizing ghost images by varying reference values until minimal expression is achieved, allowing for automated and precise material identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testing and visual inspection are used to select base materials, then material identification can be performed, but the process is subjective, time-consuming, and requires high background knowledge
Solution Approach 1:
The system performs self-calibration by automatically selecting optimal base materials through iterative processing of calibration images. The algorithm independently evaluates different base material combinations and selects the best match without requiring manual intervention or expert knowledge, enabling the system to service itself during the calibration process.
Solution Approach 2:
The system varies parameters such as base material atomic numbers, energy spectrum ranges, and decomposition weights iteratively to find the optimal configuration. By systematically changing these parameters and evaluating results through ghost image minimization, the system automatically identifies the best base material selection without manual testing.
2Productivity
If approximate base materials are used for materials of unknown composition, then processing can proceed, but errors and ghost images occur in the evaluation
Solution Approach 1:
The system uses feedback from the evaluation process itself to improve base material selection. By calculating ghost images and measuring their strength, the system receives feedback on how well the current base materials match the actual materials in the sample, then iteratively adjusts the base material selection to minimize ghost images and improve accuracy.
Solution Approach 2:
The system performs preliminary calibration processing on calibration images before actual material evaluation. This preliminary action involves testing different base material combinations and selecting the optimal set beforehand, so that when actual samples are processed, the system already has accurate base material definitions ready, improving both speed and reliability.
3Extent of automation
If iterative processing with varied reference values is performed to minimize ghost images, then base material selection becomes systematic and automated, but processing time increases
Solution Approach 1:
The system performs a limited number of iterative processing cycles rather than exhaustive testing of all possible base material combinations. By implementing stop criteria that terminate the iteration process when sufficient accuracy is achieved or when a maximum number of iterations is reached, the system performs partial action that balances automation with processing time constraints.
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 enables systematic and automated selection of base materials, reducing ghost images and improving the accuracy of material identification in multi-energy X-ray imaging, making the process more efficient and less dependent on human expertise.
Implementation Method 1
obtaining a multi-energy (e.g. two-dimensional) x-ray image of a material comprising a first (related) material area associated with a first material and a second (related) material area associated with a second material
Data Source
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AI summary
Exemplary embodiments of the present invention provide a method for evaluating a multi-energy X-ray image using base material decomposition (BMD). The method comprises the four basic steps a) to d): a) Obtaining a multi-energy X-ray image of a material, comprising a first material region belonging to a first material and a second material region belonging to a second material; b) Processing the multi-energy X-ray image by comparing the transmission values measured in each region during irradiation of the material with at least a first and a second reference value, wherein the first reference value is assigned to a first base material and the second reference value to a second base material, in order to obtain a first representation for the regions belonging to the first base material and a second representation for the regions belonging to the second base material;c, d) Evaluate the first and second images with regard to the expression of a first region resulting from the irradiation of the first material region and with regard to the expression of a second region resulting from the irradiation of the second material region; These steps b) to d) are repeated with varied first and/or varied second reference values assigned to other first and/or other second base materials in order to obtain further first and further second images. The evaluation is then carried out until the expression of the second region in the further first images and/or until the expression of the first region in the further second images is minimal or minimal in magnitude.