A deep learning approach for dual-energy CT imaging using a single-energy CT data

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. The end point of the deep learning approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. We retrospectively studied 22 patients who received contrast-enhanced abdomen DECT scan. The difference between the predicted and original high-energy CT images are 3.47 HU, 2.95 HU, 2.38 HU, and 2.40 HU for spine, aorta, liver and stomach, respectively. The difference between virtual non-contrast (VNC) images obtained from original DECT and deep learning DECT are 4.10 HU, 3.75 HU, 2.33 HU and 2.92 HU for spine, aorta, liver and stomach, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9%. 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.