  Purpose/Objectives: To develop and assess a strategy of using deep learning
(DL) to generate virtual monochromatic CT (VMCT) images from a single-energy CT
(SECT) scan. Materials/Methods: The proposed data-driven VMCT imaging consists
of two steps: (i) using a supervised DL model trained with a large number of
100 kV and 140 kV dual-energy CT (DECT) image pairs to produce the
corresponding high-energy CT image from a low-energy image; and (ii)
reconstructing VMCT images with energy ranging from 40 to 150 keV. To evaluate
the performance of the method, we retrospectively studied 6,767 abdominal DECT
images. The VMCT images reconstructed using both DL-derived DECT (DL-DECT)
images and the images from DECT scanner were compared quantitatively.
Paired-sample t-tests were used for statistical analysis to show the
consistency and precision of calculated HU values. Results: Excellent agreement
was found between the DL-DECT and the ground truth DECT images (p values ranged
from 0.50 to 0.95). Noise reduction up to 68% (from 163 HU to 51 HU) was
achieved for DL-based VMCT imaging as compared to that obtained by using the
standard DECT. For the DL-based VMCT, the maximum iodine contrast-to-noise
ratio (CNR) for each patient (ranging from 15.1 to 16.6) was achieved at 40
keV. In addition to the enormous benefit of VMCT acquisition with merely a SECT
image, an improvement of CNR as high as 55% (from 10.7 to 16.6) was attained
with the proposed approach. Conclusions: This study demonstrates that
high-quality VMCT images can be obtained with only a SECT scan.
