Multi-Energy CT Artifact Estimation for Reliable Material Decomposition
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Solution Overview
Problem
Existing multi-energy computed tomography (CT) techniques struggle with image artifacts caused by acquisition techniques, patient size, and motion, which amplify errors in material decomposition processes, making it difficult for physicians to trust quantitative information in result datasets.
Innovation Solution
A computer-implemented method that estimates and corrects errors due to image artifacts by identifying subregions free from target materials with known energy dependencies, comparing image values, and interpolating/extrapolating deviation values to determine artifact strength, providing additional error information or corrections in the result dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If material decomposition is performed to extract quantitative information, then diagnostic capability is improved, but artifact errors are amplified making results unreliable
Solution Approach 1:
The imaging region is segmented into first subregions (containing target material) and second subregions (free from target material). Artifact estimation is performed separately in second subregions where target material is absent, then extrapolated to first subregions. This segmentation allows artifact quantification without contamination from target material signal variations.
Solution Approach 2:
Second subregions free from target material serve as intermediary zones for artifact estimation. These regions act as mediators that contain artifact information without the confounding presence of target material, enabling clean measurement of artifact characteristics that can then be applied to correct the target material regions.
2Reliability
If additional scans are performed to verify results, then diagnostic confidence is improved, but examination time and radiation exposure increase
Solution Approach 1:
The method provides feedback by calculating and providing artifact information alongside the quantitative result values. This feedback mechanism allows physicians to assess the reliability of results without requiring additional scans, as the artifact estimation directly indicates the quality and reliability of the material decomposition results.
Solution Approach 2:
The system performs self-verification by automatically estimating and providing artifact information as part of the same scan process. Instead of requiring separate verification scans, the system uses the acquired data itself to quantify and communicate the reliability of results, making the examination process self-sufficient.
3Measurement precision
If artifact estimation is performed in all regions, then accuracy is improved, but computational complexity increases
Solution Approach 1:
The computational domain is segmented into first subregions (with target material) and second subregions (without target material). Artifact estimation computations are performed only in second subregions where the problem is simpler and target material does not interfere, then the results are extrapolated to first subregions. This segmentation significantly reduces computational complexity while maintaining accuracy.
Solution Approach 2:
Instead of performing artifact estimation throughout the entire imaging region, the method applies partial action by computing artifacts only in second subregions free from target material. This partial computation approach reduces computational burden while the results are still sufficient (via extrapolation) to characterize artifacts in the target material regions.
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
Enhances the quality of multi-energy CT result datasets by accurately estimating and correcting artifacts, improving the reliability of quantitative information and reducing errors, thereby increasing confidence in diagnostic results.
Implementation Method 1
at least two three-dimensional energy datasets of an imaging region of a patient are acquired using an x-ray imaging device, in particular a computed tomography device, using different x-ray energy spectra
Implementation Method 2
comparing the image values of the energy dataset for each respective first subregion and for each respective voxel, taking into account the known energy dependence, to determine deviation values indicative of artifacts
Data Source
AI summary
A computer-implemented method of an embodiment is for automatically estimating and/or correcting an error due to artifacts in a multi-energy computed tomography result dataset relating to at least one target material. In an embodiment, the method includes determining at least one first subregion of the imaging region, which is free from the target material and contains at least one, in particular exactly one, second material with known material-specific energy dependence of x-ray attenuation; for each first subregion, comparing the image values of the energy dataset for each voxel, taking into account the known energy dependence, to determine deviation values indicative of artifacts; and for at least a part of the at least one remaining second subregion of the imaging region, calculating estimated deviation values by interpolating and/or extrapolating from the determined deviation values in the first subregion, the estimated deviation values being used as estimated error due to artifacts.