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• W2803663917 abstract "The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρe), mean excitation energy (Ix), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation." @default.
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• W2803663917 date "2018-06-08" @default.
• W2803663917 modified "2023-10-14" @default.
• W2803663917 title "Machine learning-based dual-energy CT parametric mapping" @default.
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• W2803663917 doi "https://doi.org/10.1088/1361-6560/aac711" @default.
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