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

Dual-energy CT Reconstruction from Dual Quarter Scans