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By acquiring two sets of tomographic measurements at distinct X-ray spectra,the dual-energy CT (DECT) enables quantitative material-specific imaging.However, the conventionally decomposed material basis images may encountersevere image noise amplification and artifacts, resulting in degraded imagequality and decreased quantitative accuracy. Iterative DECT imagereconstruction algorithms incorporating either the sinogram or the CT imageprior information have shown potential advantages in noise and artifactsuppression, but with the expense of large computational resource, prolongedreconstruction time, and tedious manual selections of algorithm parameters. Topartially overcome these limitations, we develop a domain-transformationenabled end-to-end deep convolutional neural network (DIRECT-Net) to performhigh quality DECT material decomposition. Specifically, the proposed DIRECT-Nethas immediate accesses to mutual-domain data, and utilizes stacked convolutionneural network (CNN) layers for noise reduction and material decomposition. Thetraining data are numerically simulated based on the underlying physics of DECTimaging.The XCAT digital phantom, iodine solutions phantom, and biologicalspecimen are used to validate the performance of DIRECT-Net. The qualitativeand quantitative results demonstrate that this newly developed DIRECT-Net ispromising in suppressing noise, improving image accuracy, and reducingcomputation time for future DECT imaging.

DIRECT-Net: a unified mutual-domain material decomposition network for quantitative dual-energy CT imaging