Adaptive X-ray Detector Noise Filtering
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Solution Overview
Problem
Conventional X-ray image recording devices have fixed filter functions that do not account for individual properties of the detectors, leading to inadequate noise suppression and image quality, especially due to variations in detector manufacturing and noise behavior.
Innovation Solution
A method that involves obtaining detector data without an image object to evaluate noise levels, setting filtering variables based on statistical noise metrics like standard deviation, and applying adaptive spatial and temporal filtering processes to produce noise-free images tailored to the specific X-ray beam detector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed filter functions are used in conventional X-ray image recording devices, then the device complexity is reduced and ease of manufacture is improved, but the noise suppression effectiveness deteriorates and image quality worsens due to inability to account for individual detector properties
Solution Approach 1:
The filter function is transformed from a fixed, static configuration to a dynamic, adaptive system. The filtering parameters are no longer predetermined but are determined in real-time based on measured detector properties (noise characteristics, gain factors) and imaging conditions (X-ray dose, temporal neighborhood). This allows the filtering process to adapt to each specific detector instance and imaging scenario, resolving the contradiction between manufacturing simplicity and noise suppression effectiveness.
Solution Approach 2:
The invention changes the parameters of the filtering process from fixed values to variable parameters that depend on measured detector characteristics. Specifically, the filter strength, spatial neighborhood size, and temporal neighborhood size are adjusted based on the standard deviation of detector signals and other measured properties. This parameter adaptation enables optimal noise suppression for each detector while maintaining ease of manufacture through automated measurement and adjustment.
2Object-affected harmful factors
If strong averaging is applied in filtering processes to reduce noise effects, then noise suppression is improved, but image signals are smudged and image quality deteriorates
Solution Approach 1:
The filtering process applies different averaging strengths to different regions of the image based on local characteristics. The spatial neighborhood and temporal neighborhood are adjusted locally according to the measured standard deviation in each region. In regions with high standard deviation (likely containing image signals), less averaging is applied to preserve signal clarity. In regions with low standard deviation (likely noise-dominated), stronger averaging is applied to suppress noise. This local adaptation resolves the contradiction between noise suppression and signal clarity.
Solution Approach 2:
The filtering process incorporates feedback from measured detector properties and intermediate image analysis. The standard deviation of detector signals is measured and used to feedback-adjust the filtering parameters. The system continuously monitors the effect of filtering and adjusts the averaging strength accordingly, preventing excessive smudging of image signals while maintaining noise suppression. This feedback mechanism enables dynamic optimization of the noise-signal trade-off.
3Device complexity
If fixed filter functions are prescribed for particular device models, then device complexity is reduced, but adaptability to individual detector properties deteriorates
Solution Approach 1:
The system performs self-characterization by automatically measuring its own detector properties (noise characteristics, gain factors, temporal correlations) without requiring external calibration or complex setup procedures. The filter function adapts to the specific detector instance through self-measurement and self-adjustment. This self-service approach maintains low device complexity while achieving high adaptability to individual detector properties, as the system configures itself automatically based on measured properties.
Solution Approach 2:
The system performs preliminary measurement of detector properties (noise standard deviation, gain factors, temporal neighborhoods) before the actual imaging process. These preliminary measurements are used to pre-configurate the filter function for optimal performance with that specific detector. This preliminary action enables the system to adapt to individual detector characteristics without adding complexity to the main imaging workflow, as the adaptation is completed in advance.
Data Source
AI summary
X-ray beam detectors in one model can individually differ from one another. This can lead to differences in the amount of noise in an image recorded with the aid of the respective X-ray beam detector. In the present case, a variable is derived using an empty image, which variable reproduces the amount of noise, and this variable then determines the type and extent of a filtering process. Hence the image processing is adapted to the respective individual noise behavior of the respective X-ray beam detector. This is particularly suitable if the X-ray beam detector is a flat-panel detector (100) with a scintillator (22) and photodetector elements (12).