X-Ray Noise Variance Stabilization Using Detector Physics
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Solution Overview
Problem
Existing X-ray image processing pipelines face challenges in handling varying noise characteristics due to different contributions of Poisson and Gaussian noise, requiring different algorithm variants for different imaging scenarios, and data-driven approaches are time-critical and unstable in scenarios with significant structure or intensity variation.
Innovation Solution
A method that stabilizes noise variance by simulating energy deposition on the X-ray detector using physical models to determine a noise level parameter, allowing for a variance-stabilizing transformation, such as the generalized Anscombe transform, to normalize noise variance, independent of imaging parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a variance-stabilizing transformation is applied to normalize noise variance, then noise variance stabilization is achieved, but additional computational effort is required to determine noise parameters
Solution Approach 1:
The patent pre-calculates and stores noise parameters (such as electronic noise variance) during system calibration or initialization phases. These pre-computed parameters are then reused during real-time image processing without recalculation, significantly reducing the computational burden during actual operation while maintaining accurate noise variance stabilization.
Solution Approach 2:
The patent uses measured or simulated noise characteristics to create representative models or lookup tables that approximate the actual noise behavior under various imaging conditions. These copied noise models are then applied during image processing instead of performing complex real-time noise analysis, reducing computational effort while preserving stabilization accuracy.
2Adaptability or versatility
If data-driven approaches are used for noise estimation, then adaptive noise characterization is achieved, but stability problems occur in scenarios with large structure or intensity variation
Solution Approach 1:
The patent introduces physical models of the X-ray imaging system as intermediary components between the raw image data and the noise estimation process. These physics-based models (incorporating Poisson statistics for quantum noise and Gaussian models for electronic noise) serve as mediators that provide stable, theory-driven noise estimates independent of image content variations, preventing the instability that plagues purely data-driven approaches.
Solution Approach 2:
The patent transitions from content-dependent noise estimation (data-driven) to parameter-driven noise estimation (physics-based). By changing the fundamental approach from analyzing image pixel variations to using known physical parameters of the imaging system (exposure settings, detector characteristics), the method achieves stability across all imaging scenarios including those with large structures or intensity variations.
3Measurement precision
If different algorithm variants are provided for different noise regimes, then processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent develops a universal noise modeling framework that handles multiple noise regimes (Poisson quantum noise, Gaussian electronic noise, and their combinations) through a single unified approach. By expressing total noise as the quadrature sum of independent noise sources with parameters that adapt to different imaging conditions, the system eliminates the need for separate algorithm variants while maintaining processing accuracy across all scenarios.
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 reliability and accuracy of noise variance stabilization, reducing computational effort and enabling consistent noise variance for various imaging scenarios, facilitating robust image processing algorithms like denoising, edge enhancement, and segmentation.
Implementation Method 1
an X-ray image, which is or has been generated by an X-ray imaging system (1), and a corresponding set of imaging parameters (14) of the X-ray imaging system (1) are received
Implementation Method 2
A noise level parameter (16) is computed by simulating an energy deposition of X-ray quanta emitted by an X-ray source (3) of the X-ray imaging system (1) on an X-ray detector (4)
Data Source
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AI summary
For generating a noise-variance-stabilized X-ray image (18), an X-ray image (13) generated by an X-ray imaging system (1) and corresponding set of imaging parameters (14) of the X-ray imaging system (1) are received. A noise level parameter (16) is computed by simulating an energy deposition of X-ray quanta emitted by an X-ray source (3) of the X-ray imaging system (1) on an X-ray detector (4) of the X-ray imaging system (1) depending on the set of imaging parameters (14). The noise-variance-stabilized X-ray image (18) is generated by applying a variance-stabilizing transformation, which depends on the noise level parameter, to the X-ray image (13).