Pixel Detector Correction Tables for Variable X-Ray Conditions
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Solution Overview
Problem
Existing correction tables for X-ray intensity data measured by pixel detectors are limited to specific conditions and require re-adjustment when environmental changes occur, such as temperature variations or changes in X-ray sources, leading to inefficiencies and the need for re-preparation of correction tables.
Innovation Solution
A data processing apparatus and method that generates correction tables dynamically based on input measurement conditions, using approximate formulas to account for factors like temperature, X-ray source, and noise, allowing for efficient correction of X-ray intensity data without requiring re-adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of pixels in an image is increased to improve resolution, then image quality is improved, but the burden on the display device and processing time increase
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) based on pixel characteristics. Only pixels within these ROIs are processed in detail, while other pixels are handled more simply. This segmentation allows high resolution to be maintained in important areas without processing the entire high-resolution image, thereby reducing the display device burden.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their importance. Regions containing important features (detected through template matching or other methods) receive full high-resolution processing, while less important regions use simplified processing. This local quality approach maintains overall image quality while reducing computational burden.
2Measurement precision
If the number of pixels in an image is increased to improve resolution, then image quality is improved, but processing time increases
Solution Approach 1:
The high-resolution image is segmented into multiple regions of interest. Processing is performed separately on each ROI rather than on the entire image. This reduces the total processing time while maintaining high resolution in the important regions, as fewer pixels require intensive processing.
Solution Approach 2:
Instead of processing all pixels at full resolution, the method applies full processing only to necessary regions (partial action). Other regions use reduced processing, which is sufficient for their importance level. This partial action approach significantly reduces processing time while maintaining acceptable overall image quality.
3Measurement precision
If a high-resolution image is used, then image quality is improved, but bandwidth requirements increase
Solution Approach 1:
The image data is segmented into regions of interest and non-ROI areas. Only the ROI portions are transmitted or processed at full high resolution, while other areas use lower resolution or compressed formats. This segmentation reduces the total data bandwidth required while preserving image quality in important regions.
Solution Approach 2:
Different quality levels are applied locally to different image regions. High-quality full-resolution data is provided only where needed (in ROIs), while other regions use lower quality representations. This local quality differentiation reduces overall bandwidth consumption while maintaining perceived image quality.
Data Source
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AI summary
Provided are a data processing apparatus that saves a trouble of setting a correction table again and enables a user to measure X-ray intensity data quickly under a desired condition, a method of obtaining the characteristic of each pixel and a method of data processing, and a program. A data processing apparatus 100 to correct X-ray intensity data measured by a pixel detector includes a characteristic storage unit 130 to store the characteristic of each pixel in a specific detector, a correction table generation unit 120 to apply a measurement condition input as that in measurement by a specific detector and a value expressing the characteristic of each pixel to an approximate formula expressing the count value of each pixel and to generate a correction table for the specific detector using the calculation result of the approximate formula, and a correction unit 160 to correct the X-ray intensity data measured by the specific detector using the generated correction table.