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Dual-energy computed tomography (DECT) is of great significance for clinicalpractice due to its huge potential to provide material-specific information.However, DECT scanners are usually more expensive than standard single-energyCT (SECT) scanners and thus are less accessible to undeveloped regions. In thispaper, we show that the energy-domain correlation and anatomical consistencybetween standard DECT images can be harnessed by a deep learning model toprovide high-performance DECT imaging from fully-sampled low-energy datatogether with single-view high-energy data, which can be obtained by using ascout-view high-energy image. We demonstrate the feasibility of the approachwith contrast-enhanced DECT scans from 5,753 slices of images of twenty-twopatients and show its superior performance on DECT applications. The deeplearning-based approach could be useful to further significantly reduce theradiation dose of current premium DECT scanners and has the potential tosimplify the hardware of DECT imaging systems and to enable DECT imaging usingstandard SECT scanners.

Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network