  In a standard computed tomography (CT) image, pixels having the same
Hounsfield 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 are
not widely available as single-energy CT (SECT) scanners. Here we develop a
deep learning approach to perform DECT imaging by using standard SECT data. A
deep learning model to map low-energy image to high-energy image using a
two-stage convolutional neural network (CNN) is developed. The model was
evaluated using patients who received contrast-enhanced abdomen DECT scan with
a popular DE application: virtual non-contrast (VNC) imaging and contrast
quantification. The HU differences between the predicted and original
high-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 images
obtained from original DECT and deep learning DECT are 4.10, 3.75, 2.33 and
2.92 HU for the 4 ROIs, respectively. The aorta iodine quantification
difference between iodine maps obtained from original DECT and deep learning
DECT images is 0.9\%, suggesting high consistency between the predicted and the
original high-energy CT images. This study demonstrates that highly accurate
DECT imaging with single low-energy data is achievable by using a deep learning
approach. The proposed method can significantly simplify the DECT system
design, reducing the scanning dose and imaging cost.
