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Results indicate that spectral CT adds significant discrimination power, in particular when utilizing local spectral variances and covariances, which can be computed efficiently by standard Gaussian filter operations. Simple linear spectral material separation, however, is sufficient only in extended homogeneous regions. In subtle finely structured transition areas, non-linear classifiers or convolutional neural networks are required because of non-linear local multi material superposition effects. |
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