X-ray Spectral Attenuation Curve k-Edge Substitution
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Solution Overview
Problem
Current X-ray computed tomography (CT) imaging systems face challenges in improving signal-to-noise ratio and effective Z estimation, particularly in medical and industrial applications, where existing methods often result in reduced accuracy due to k-edge features in spectral attenuation curves and artifacts in projection data.
Innovation Solution
The system employs processors to determine and modify spectral attenuation curves by substituting k-edge features with approximations, constructing a material decomposition model, and correcting projection sets to enhance image quality and reduce artifacts, thereby improving signal-to-noise ratio and effective atomic number estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If k-edge features are included in spectral attenuation curves for material decomposition, then measurement precision of material composition is improved, but manufacturing precision of the decomposition model deteriorates due to inaccuracies and artifacts
Solution Approach 1:
The patent applies preliminary action by pre-processing the spectral attenuation curves to identify and correct for beam hardening effects and k-edge artifacts before performing material decomposition. This advance preparation removes systematic errors that would otherwise degrade the accuracy of the decomposition model, allowing both high measurement precision and model manufacturing precision to be achieved.
Solution Approach 2:
The patent employs parameter changes by adjusting the energy binning strategy and modifying the spectral attenuation curve parameters to optimize the material decomposition process. By changing how the spectral data is binned and processed, the system maintains sensitivity to material composition differences while reducing the impact of k-edge artifacts and beam hardening effects on model accuracy.
2Device complexity
If conventional material decomposition methods are used, then device complexity is reduced, but measurement precision of effective atomic number deteriorates due to k-edge related inaccuracies
Solution Approach 1:
The patent replaces conventional mechanical/mathematical decomposition methods with a machine learning-based approach. Neural networks are trained to recognize patterns in spectral attenuation data and directly predict material composition and effective atomic number, substituting traditional iterative mathematical algorithms with a data-driven model that is more robust to k-edge artifacts and beam hardening effects.
Solution Approach 2:
The patent uses a composite approach by combining multiple processing techniques: spectral binning, artifact correction algorithms, and machine learning models work together in an integrated pipeline. This composite method leverages the strengths of each technique to achieve high measurement precision for effective atomic number while maintaining manageable system complexity through modular architecture.
3Reliability
If spectral attenuation curves with k-edge features are used, then reliability of material identification is improved, but object-affected harmful factors increase due to artifacts and inaccuracies in projection data
Solution Approach 1:
The patent applies preliminary anti-action by implementing correction algorithms that specifically target and counteract k-edge artifacts and beam hardening effects before material identification is performed. These pre-processing steps actively remove the harmful artifacts that would otherwise compromise the reliability of material identification, allowing the system to maintain high reliability while minimizing object-affected harmful factors.
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 results in improved signal-to-noise ratio and high-fidelity effective atomic number estimates, reducing acquisition times and enhancing image quality by using modified spectral attenuation curves and correcting projection sets, specifically addressing k-edge related inaccuracies and artifacts.
Implementation Method 1
based on the attenuation of X-rays between the X-ray source and the X-ray detector by the subject undergoing imaging
Implementation Method 2
the underlying physical effects of the interaction of the X-rays with the subject of interest may be discerned, namely, the scattering effects and photoelectric effects
Implementation Method 3
the underlying physical effects of the interaction of the X-rays with the subject of interest may be discerned, namely, the scattering effects and photoelectric effects
Data Source
AI summary
An energy-sensitive system includes one or more processors configured to determine spectral attenuation curves for a first basis material and a second basis material, respectively. The one or more processors are configured to substitute a k-edge feature in the determined spectral attenuation curves with an approximation of the determined spectral attenuation curves lacking the k-edge feature. The one or more processors are also configured to construct a material decomposition model based on one of the determined or approximated first and second spectral attenuation curves. The one or more processors are additionally configured to decompose X-ray projection data into basis material projection data comprising first and second line integrals based, at least in part, on the model.