Multienergetic X-ray Voxel Classification for Contrast Medium Differentiation
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Solution Overview
Problem
In digital subtraction angiography, osseous, metal, and calciferous structures with similar X-ray absorption to contrast media can lead to errors in determining difference image data records due to their similarity in X-ray absorption, making it challenging to accurately differentiate and determine these structures.
Innovation Solution
A method using a biplanar X-ray device with multiple X-ray sources and detectors to receive X-ray projections at different energies, allowing for the creation of multienergetic real image data records, which enables the selection of voxels that map contrast medium, thereby providing more accurate constraint and difference image data records through the use of trained functions and algorithms like polyenergetic reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mask recordings are combined with contrast medium recordings to suppress structures in digital subtraction angiography, then vessel visualization is improved, but X-ray burden on the examination volume increases
Solution Approach 1:
The system performs preliminary classification of voxels using a trained function before subtraction, identifying contrast medium voxels and constraint voxels in advance. This preliminary action enables selective processing that reduces the need for additional mask recordings, thereby lowering X-ray burden while maintaining vessel visualization quality
Solution Approach 2:
The system changes the energy parameter of X-ray projections by acquiring data at multiple energy levels. This parameter change enables material differentiation based on energy-dependent attenuation characteristics, improving vessel visualization through enhanced contrast between blood and surrounding structures without requiring increased X-ray dosage
2Device complexity
If structures with similar X-ray absorption (osseous, metal, calciferous) are present in the examination region, then X-ray imaging is simplified, but errors in difference image data record determination increase
Solution Approach 1:
The system acquires X-ray projections at multiple energy levels and uses the energy-dependent attenuation differences to distinguish between materials with similar absorption at a single energy. By analyzing how different materials attenuate X-rays at varying energies, the system can differentiate contrast medium from osseous, metal, and calciferous structures, thereby improving difference image accuracy
Solution Approach 2:
The trained function acts as an intermediary classifier that processes multienergetic voxel data and assigns material labels. This intermediary classification step separates contrast medium voxels from other structures before the subtraction process, preventing errors that would otherwise occur due to similar X-ray absorption characteristics
3Measurement precision
If a trained function is applied to classify voxels in multienergetic real image data records, then material differentiation accuracy is improved, but computational complexity increases
Solution Approach 1:
The trained function performs preliminary classification of all voxels into contrast medium, constraint, and other structures before the subtraction operation. This preliminary classification organizes the data in advance, enabling efficient processing in subsequent steps and reducing the computational burden during the actual difference image calculation
Solution Approach 2:
The system segments the examination volume into distinct voxel categories (contrary medium voxels, constraint voxels, other voxels) based on their attenuation characteristics across multiple energies. This segmentation divides the complex problem of material differentiation into manageable classified groups, simplifying the subsequent subtraction and image reconstruction processes
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 the accuracy and reduces errors in determining difference image data records by effectively distinguishing between materials with similar X-ray absorption values, improving the differentiation of contrast medium and other structures within the examination volume.
Implementation Method 1
osseous, metal, and calciferous structures with similar X-ray absorption to contrast media
Data Source
AI summary
A computer-implemented method includes, in an embodiment, receiving first X-ray projections of an examination volume in respect of a first X-ray energy and second X-ray projections in respect of a second X-ray energy, the first and second X-ray energies differing. The method further includes determination of a multienergetic real image data record of the examination volume based upon the first and second X-ray projections; selection of first voxels of the multienergetic real image data record based upon the multienergetic real image data record; selection of second voxels of the multienergetic real image data record based upon the first X-ray projections and the second X-ray projections, the first voxels including the second voxels and the second voxels mapping contrast medium in the examination volume. The method further includes provision of a constraint image data record and/or a difference image data record based upon the second voxels.