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In a standard computed tomography (CT) image, pixels having the sameHounsfield Units (HU) can correspond to different materials and it is,therefore, challenging to differentiate and quantify materials. Dual-energy CT(DECT) is desirable to differentiate multiple materials, but DECT scanners arenot widely available as single-energy CT (SECT) scanners. Here we develop adeep learning approach to perform DECT imaging by using standard SECT data. Adeep learning model to map low-energy image to high-energy image using atwo-stage convolutional neural network (CNN) is developed. The model wasevaluated using patients who received contrast-enhanced abdomen DECT scan witha popular DE application: virtual non-contrast (VNC) imaging and contrastquantification. The HU differences between the predicted and originalhigh-energy CT images are 3.47, 2.95, 2.38 and 2.40 HU for ROIs on the spine,aorta, liver, and stomach, respectively. The HU differences between VNC imagesobtained from original DECT and deep learning DECT are 4.10, 3.75, 2.33 and2.92 HU for the 4 ROIs, respectively. The aorta iodine quantificationdifference between iodine maps obtained from original DECT and deep learningDECT images is 0.9\%, suggesting high consistency between the predicted and theoriginal high-energy CT images. This study demonstrates that highly accurateDECT imaging with single low-energy data is achievable by using a deep learningapproach. The proposed method can significantly simplify the DECT systemdesign, reducing the scanning dose and imaging cost.

Dual-energy CT imaging using a single-energy CT data is feasible via deep learning