Scan Image Quality Index Quantification
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Solution Overview
Problem
Current methods for assessing image quality, particularly in CT scans, are complex and difficult to administer, making it challenging to effectively quantify and improve the quality of scan images.
Innovation Solution
A computer-implemented method that combines measures of contrast and sharpness into a single quality index value (QI) to quantify the quality of scan images, allowing for the identification of regions with high and low contrast and sharpness, and adjusting scanner configurations to meet quality criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to assess image quality, then comprehensive evaluation of contrast and sharpness can be achieved, but the assessment process becomes complex and difficult to administer
Solution Approach 1:
The patent combines multiple image quality parameters (contrast and sharpness measures from different regions) into a single quality index value. This merging approach maintains comprehensive evaluation capability while simplifying the assessment process into a unified metric that is easier to administer and interpret.
Solution Approach 2:
The invention transforms complex image quality assessment by changing the parameter representation from multiple separate measures to a single composite quality index. This parameter transformation simplifies the assessment process while preserving the essential quality information through mathematical combination of contrast and sharpness measures.
2Measurement precision
If multiple regions are analyzed to derive contrast and sharpness measures, then more accurate quality quantification is achieved, but the computational complexity increases
Solution Approach 1:
The patent divides the scan image into multiple regions (first region and second region) to derive separate contrast and sharpness measures. This segmentation enables more accurate quality assessment by capturing spatial variations in image quality, while the structured approach to combining these measures keeps computational complexity manageable.
Solution Approach 2:
The invention applies local quality assessment by deriving contrast and sharpness measures from specific first and second regions of the image rather than treating the entire image uniformly. This local analysis approach improves quantification accuracy by accounting for regional differences in image quality characteristics.
3Ease of operation
If a single quality index value is used to represent image quality, then ease of operation is improved, but detailed information about specific quality aspects may be lost
Solution Approach 1:
The patent transforms detailed quality parameters (contrast and sharpness from multiple regions) into a single quality index value through mathematical combination. This parameter change simplifies operation and interpretation while the underlying calculation preserves the essential quality information from all input measures.
Solution Approach 2:
The invention merges multiple quality measures (first contrast measure, first sharpness measure, second contrast measure, second sharpness measure) into a single quality index value. This combining approach maintains ease of operation with a unified metric while preserving comprehensive quality information through the aggregation of multiple regional assessments.
Data Source
AI summary
A method of quantifying the quality of a scan image of at least a portion of an object against a background, the method including: deriving a first measure of contrast and a first measure of sharpness from a first region of the scan image, and a second measure of contrast and a second measure of sharpness from a second region of the scan image; and determining a quality index value indicative of the quality of the scan image based on the first measure of contrast, the first measure of sharpness, the second measure of contrast and the second measure of sharpness.