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Spectral computed tomography (CT) has become a popular clinical diagnostic technique because of its unique advantage in material distinction. Specifically, it can perform virtual monochromatic imaging to obtain accurate tissue composition with less beam hardening artifacts. It is an ill-posed problem that monochromatic images are acquired by material decomposition matrix, suffering from amplified noise due to various uncertain factors. Aiming at modeling spatial and spectral correlations, this paper proposes a Wasserstein generative adversarial network with a hybrid loss (WGAN-HL) for monochromatic imaging instead of voxel-by-voxel decomposition. A min-max concept about the optimal transport is introduced in WGAN to make a tradeoff between generated images and target images where the authenticity of data cannot be distinguished anymore by network. The hybrid loss focuses on the data distribution of the generated images and target images from voxel space together with feature space to meet clinical requirements. Thereby, the proposed network can generate robust monochromatic images with accurate decomposition at any energy, while identifying and removing noise and artifacts. The advantages of this method are demonstrated in CT value measurement, beam hardening, and metal artifacts removal. Simulations and real tests prove that the WGAN-HL method preserves the important tissue details with less noise and it can reconstruct more accurate CT value. Both qualitative and quantitative comparisons show that the network is superior to other monochromatic imaging method.INDEX TERMS Spectral CT, generative adversarial network, monochromatic imaging, hybrid loss.

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