  In clinical CT system, the x-ray tube emits polychromatic x-rays, and the
x-ray detectors operate in the current-integrating mode. This physical process
is accurately described by an energy-dependent non-linear integral equation.
However, the non-linear model is not invertible with a computationally
efficient solution, and is often approximated as a linear integral model in the
form of the Radon transform. Such approximation basically ignores
energy-dependent information and would generate beam hardening artifacts.
Dual-energy CT (DECT) scans one object using two different x-ray energy spectra
for the acquisition of two spectrally distinct projection datasets to improve
imaging performance. Thus, DECT can reconstruct energy and material-selective
images, realizing monochromatic imaging and material decomposition.
Nevertheless, DECT would increase radiation dose, system complexity, and
equipment cost relative to single-spectrum CT. In this paper, a
machine-learning-based CT reconstruction method is proposed to perform
monochromatic image reconstruction using a single-spectrum CT scanner.
Specifically, a residual neural network (ResNet) model is adapted to map a CT
image to a monochromatic counterpart at a pre-specified energy level. This
ResNet is trained on clinical dual-energy data, showing an excellent
convergence to a minimal loss. The trained network produces high-quality
monochromatic images on testing data, with a relative error of less than 0.2%.
This work has great potential in clinical DECT applications such as tissue
characterization, beam hardening correction and proton therapy planning.
