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Neural-network-based material identification in photon-counting computed tomography using region-of-interest spectral features
This work investigates the feasibility of neural-network-based material identification based on photon-counting computed tomography (PCCT) data. The input data were spectral vectors extracted from regions of interest (ROIs) in reconstructed tomographic slices of a phantom containing La-, Nd-, and Gd
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