  Compared with conventional single-energy computed tomography (CT),
dual-energy CT (DECT) provides better material differentiation but most DECT
imaging systems require dual full-angle projection data at different X-ray
spectra. Relaxing the requirement of data acquisition is a particularly
attractive research to promote the applications of DECT in a wide range of
imaging areas. In this work, we design a novel DECT imaging scheme with dual
quarter scans and propose an efficient method to reconstruct the desired DECT
images from dual limited-angle projection data, which enables DECT on imaging
configurations with half-scan and largely reduces scanning angles and radiation
doses. We first study the characteristics of image artifacts under dual quarter
scans scheme, and find that the directional limited-angle artifacts of DECT
images are complementarily distributed in image domain because the
corresponding X-rays of high- and low-energy scans are orthogonal. Inspired by
this finding, a fusion CT image is generated by integrating the limited-angle
DECT images of dual quarter scans. This strategy largely reduces the
limited-angle artifacts and preserves the image edges and inner structures.
Utilizing the capability of neural network in the modeling of nonlinear
problem, a novel Anchor network with single-entry double-out architecture is
designed in this work to yield the desired DECT images from the generated
fusion CT image. Experimental results on the simulated and real data verify the
effectiveness of the proposed method.
