X-ray Imaging Apparatus Dual-Energy Material Segmentation
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Solution Overview
Problem
Current X-ray imaging technologies face challenges in accurately segmenting and identifying abnormal materials within objects, particularly in medical imaging, where distinguishing between different tissue densities and abnormalities like tumors is difficult due to variations in X-ray attenuation coefficients and energy bands.
Innovation Solution
An X-ray imaging apparatus and method that segment X-ray images into multiple regions, estimate the thickness of each region, and map these regions to corresponding phantom images based on dual energy X-ray data, allowing for the identification and emphasis of abnormal materials by generating a mapping image that highlights regions with contrast media or abnormal tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray imaging is performed using conventional single-energy methods, then the imaging process is simple and fast, but the ability to distinguish and identify abnormal materials is insufficient
Solution Approach 1:
The imaging process is segmented into multiple energy bands (first energy band and second energy band), allowing separate acquisition and processing of X-ray images at different energies. This segmentation enables the system to extract material-specific information from each energy band, improving the identification accuracy of abnormal materials while maintaining a structured and manageable imaging workflow
Solution Approach 2:
The system changes the energy parameter of X-rays by acquiring images at multiple energy bands. By varying the X-ray energy and comparing the attenuation characteristics across different energy levels, the system can distinguish between different materials (e.g., organic vs. inorganic substances) based on their unique energy-dependent attenuation profiles, thereby improving material identification accuracy
2Measurement precision
If dual energy X-ray imaging is used to improve material identification, then the accuracy of distinguishing abnormal materials improves, but the imaging time and processing complexity increase
Solution Approach 1:
The system performs preliminary segmentation of the X-ray image into multiple energy bands before full processing. By pre-processing the dual-energy images to separate and characterize different energy components, the system prepares material identification data in advance, which accelerates the subsequent analysis and reduces overall processing time while maintaining high material differentiation accuracy
Solution Approach 2:
The system creates virtual material maps by comparing the attenuation characteristics of different regions across dual-energy images. Instead of performing complex physical measurements or additional imaging, the system generates synthetic representations of material composition based on the dual-energy data, enabling rapid material identification without requiring additional imaging time
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 enables precise identification and display of abnormal materials within the object, improving diagnostic accuracy by segmenting images into regions corresponding to specific tissue types and densities, thereby enhancing the detection of lesions and tumors.
Implementation Method 1
an X-ray detector for detecting X-rays that have passed through the object and transforms the detected X-ray into electrical signals
Implementation Method 2
an X-ray source for emitting X-rays
Implementation Method 3
The penetration of X-rays varies according to properties of materials constituting the object, and thus, an internal structure of the object may be imaged by detecting the intensity of X-rays that have passed through the object
Data Source
AI summary
An X-ray imaging apparatus can include an X-ray detector configured to acquire X-ray data by detecting X-rays and an image processor configured to segment a first image generated based on the acquired X-ray data into two or more segmentation regions, to identify one or more materials present in one segmentation region of the two or more segmentation regions, and to acquire an image relating to an object which includes abnormal materials.