Sparse Dual-Energy Detector Arrays for Lower-Cost X-Ray Imaging
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Solution Overview
Problem
Ionizing radiation-based imaging systems, such as X-ray scanners, are costly due to the use of high-purity chemicals and rare-earth metals in scintillator-based detector elements, making them prohibitive for certain applications, and there is a need for detector arrays that are optimized for image performance metrics like resolution, penetration, and wire detection.
Innovation Solution
The use of sparse detector arrays with alternating active and inactive detector elements, combined with computational techniques to estimate data in inactive spaces, reduces manufacturing costs while maintaining image quality by optimizing performance metrics through dual-energy scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scintillator-based detector elements are used to ensure high image quality, then image performance metrics (resolution, penetration, wire detection) are improved, but manufacturing cost increases significantly due to high-purity chemicals and rare-earth metals
Solution Approach 1:
The detector array is segmented into sparse detector elements rather than a fully populated array. By strategically positioning fewer detector elements (e.g., every other element or in specific patterns), the system reduces the quantity of expensive scintillator materials needed while maintaining adequate sampling of the X-ray beam for image reconstruction.
Solution Approach 2:
The patent uses computational algorithms to estimate and reconstruct data from inactive detector spaces by copying and interpolating information from adjacent active detector elements. This allows the system to recover image data for positions where no physical detector element exists, effectively creating a virtual complete detector array through computational means.
2Ease of manufacture
If the number of detector elements is reduced to lower manufacturing cost, then manufacturing cost decreases, but image quality and measurement precision may deteriorate
Solution Approach 1:
The patent replaces the mechanical solution of having physical detector elements at every position with a computational approach. Instead of populating every detector space with expensive scintillator materials, the system uses algorithms to mathematically reconstruct the missing data, substituting computational processing for physical detector coverage.
Solution Approach 2:
The system changes the parameter of detector element density from fully populated to sparsely populated, and compensates by adjusting the computational reconstruction parameters. The algorithms are tuned to optimize image quality given the reduced physical detector coverage, transforming the problem from a physical limitation to a computational optimization task.
3Ease of manufacture
If sparse detector arrays are used to reduce cost, then manufacturing cost decreases, but data completeness and measurement precision in inactive spaces are compromised
Solution Approach 1:
The system performs preliminary computational action by estimating and filling in the missing data from inactive detector spaces before final image reconstruction. The algorithms pre-process the sparse data by predicting what the measurements would have been at inactive positions based on surrounding active elements, thereby recovering information before the imaging process is complete.
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
The sparse detector arrays achieve cost-effective imaging performance by reducing the number of expensive detector elements and using computational methods to enhance resolution, penetration, and wire detection, thus making X-ray scanners more affordable and efficient.
Implementation Method 1
Digital detector arrays may use scintillator-based detector elements that convert detected ionizing radiation into light energy.
Implementation Method 2
the light energy is subsequently converted into an electric charge using a photodetector array such as, for example, a plurality of photodiodes.
Data Source
AI summary
A detector assembly has a first detector array to detect low-energy photons and generate corresponding first scan image data. The first detector array has a first set of detector positions arranged in n rows and m columns. Alternate positions along each of the n rows and along each of the m columns are populated with detector elements. The detector assembly also comprises a second detector array to detect high-energy photons and generate corresponding second scan image data. The second detector array has a second set of detector positions arranged in N rows and M columns, and all detector positions in alternate rows are fully populated with second detector elements. A processor is configured to process the first and second scan image data, and the processing is modulated to optimize at least one of a set of image performance metrics.