Gradient-Weighted Bad Cell Correction in CT Detectors
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Solution Overview
Problem
Computed tomography (CT) imaging systems face challenges in correcting bad cell data, which leads to image artifacts and reduced resolution due to insufficient interpolation methods, especially when bad pixels occur near sharp edges or in diagonal detector geometries.
Innovation Solution
A method and apparatus that use a computer to identify bad cells, interpolate Ix and Iy values from neighbor cells, calculate local gradients gx and gy, and apply weighting factors wx and wy to calculate a corrected final value I(0,0) for each bad cell, effectively addressing the issue across both orthogonal and diagonal pixel patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear or higher order interpolation is used to correct bad cells, then correction is achieved, but image artifacts and streaks increase near sharp edges
Solution Approach 1:
The patent applies different correction strategies based on local image characteristics. Gradient-weighted interpolation is used in regions with smooth intensity variations, while avoiding interpolation near sharp edges where it would create artifacts. This local adaptation of correction quality prevents uniform application from causing streaks near boundaries.
Solution Approach 2:
The patent changes the interpolation parameter by introducing gradient weighting factors that adaptively modify the interpolation behavior. By calculating gradients from neighboring pixels and using them as weights, the correction method dynamically adjusts to local intensity variations, reducing artifacts while maintaining correction effectiveness.
2Device complexity
If conventional interpolation algorithms are used for diagonal detector geometries, then processing is simpler, but streaks and artifacts cannot be completely eliminated
Solution Approach 1:
The patent modifies the interpolation parameters by incorporating gradient calculations and adaptive weighting factors. This changes the conventional equal-weight interpolation to a gradient-weighted approach that better handles diagonal geometries, eliminating artifacts while maintaining reasonable processing complexity through efficient gradient computation.
Solution Approach 2:
The patent introduces dynamic adaptation to detector geometry by calculating gradients and adjusting interpolation weights based on local intensity variations. This dynamic approach automatically adapts to diagonal geometries without requiring separate processing paths, maintaining simplicity while improving artifact elimination.
3Reliability
If blocks of bad cells occur in the detector array, then correction becomes more difficult, but the patent achieves effective correction
Solution Approach 1:
The patent segments the correction process into gradient calculation, weighting factor determination, and interpolation steps. For blocks of bad cells, it processes each bad cell individually using its own local gradient and weight factors, rather than attempting to correct the entire block uniformly. This segmentation makes complex block correction manageable and effective.
Solution Approach 2:
The patent applies local gradient-weighted interpolation to each bad cell based on its specific neighborhood characteristics. Even when bad cells occur in blocks, each cell receives correction tailored to its local intensity variations and neighbor quality, maintaining high reliability without requiring overly complex global correction algorithms.
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 significantly reduces image artifacts and improves resolution by accurately correcting bad cell data, as demonstrated by a Modulation Transfer Function (MTF) analysis showing minimal resolution loss compared to systems without bad cells.
Implementation Method 1
a scintillator for converting x-rays to light energy adjacent the collimator
Implementation Method 2
photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom
Data Source
AI summary
An imaging system includes a two-dimensional detector having a plurality of cells wherein each cell is configured to detect energy or signal passing through an object. The imaging system includes a computer programmed to acquire imaging data for the plurality of cells, identify a cell to be corrected, based on the imaging data, interpolate Ix and Iy for the identified cell based on neighbor cells, and calculate local gradients gx and gy between the identified cell and its neighbor cells based on the interpolation. The computer is further programmed to calculate weighting factors wx and wy based on the local gradients, calculate a corrected final value I(0,0) for the identified cell, and correct the identified cell with the corrected final value.