CT Image Processing Apparatus for Automatic VMI Selection
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Solution Overview
Problem
Current CT image processing technologies face challenges in clearly distinguishing different substances within an object, particularly in determining the optimal energy level for the highest contrast-to-noise ratio (CNR) for lesion observation, which affects diagnostic accuracy.
Innovation Solution
A CT image processing apparatus that generates multiple virtual monochromatic images (VMIs) corresponding to various energy levels, sets a region of interest (ROI), and determines the energy level with the maximum CNR using a look-up table (LUT) based on the type and concentration of contrast agents, allowing for automatic selection and display of the optimal VMI for improved diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple virtual monochromatic images at different energy levels are generated, then the contrast-to-noise ratio (CNR) for lesion observation can be optimized, but the complexity of image processing and selection increases
Solution Approach 1:
The system automatically determines the optimal energy level by calculating CNR values for multiple VMIs and selecting the maximum without requiring manual intervention. The processor autonomously performs the selection based on predefined criteria, making the system self-sufficient in optimizing image quality.
Solution Approach 2:
The system varies the energy level parameter across multiple virtual monochromatic images and evaluates CNR at each energy level. By changing this critical parameter and selecting the optimal value, the system achieves maximum contrast-to-noise ratio automatically.
2Ease of operation
If manual selection of optimal energy level is required, then processing simplicity is maintained, but user convenience and diagnostic accuracy decrease
Solution Approach 1:
The system performs automatic optimal energy level determination without requiring user intervention in the selection process. The processor independently evaluates all VMIs and selects the best one, eliminating manual operation while enhancing both convenience and accuracy.
Solution Approach 2:
The manual mechanical selection process is replaced with an automated computational system that calculates CNR values and determines the optimal energy level algorithmically, substituting human judgment with objective mathematical evaluation.
3Measurement precision
If automatic determination of optimal energy level is implemented, then user convenience and diagnostic accuracy are improved, but the complexity of the processing system increases
Solution Approach 1:
The processing system automatically determines the optimal energy level through embedded algorithms that calculate CNR and select the maximum, making the system self-sufficient and reducing the need for external intervention while maintaining high diagnostic accuracy.
Solution Approach 2:
The system systematically varies energy level parameters across multiple VMIs and evaluates diagnostic quality metrics at each level, using parameter optimization to achieve maximum diagnostic accuracy through automated selection.
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 user convenience and diagnostic accuracy by automatically determining the energy level for the maximum CNR among VMIs, improving the clarity of lesion observation and reducing the need for manual selection.
Implementation Method 1
detect X-rays having different energy spectra transmitted through an object and obtain raw data in each of energy ranges of the X-rays
Data Source
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AI summary
A computed tomography (CT) image processing apparatus and a CT image processing method are provided. The CT image processing apparatus may generate a virtual monochromatic image (VMI) by applying a weight to each of first, second, and third images corresponding to three different energy ranges. The CT image processing apparatus may set a region of interest (ROI) on a CT image, determine a VMI at an energy level at which a CNR of the ROI is at a maximum among a plurality of VMIs, and display the determined VMI.