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Contrast materials are often injected into body to contrast specific tissues in Computed Tomography (CT) images. Contrast Enhanced CT (CECT) images obtained in this way are more useful than Non-Enhanced CT (NECT) images for medical diagnosis, but not available for everyone due to side effects of the contrast materials. Motivated by this, we develop a neural network that takes NECT images and generates their CECT counterparts. Learning such a network is extremely challenging since NECT and CECT images for training are not aligned even at the same location of the same patient due to movements of internal organs. We propose a two-stage framework to address this issue. The first stage trains an auxiliary network that removes the effect of contrast enhancement in CECT images to synthesize their NECT counterparts well-aligned with them. In the second stage, the target model is trained to predict the real CECT images given a synthetic NECT image as input. Experimental results and analysis by physicians on abdomen CT images suggest that our method outperforms existing models for neural image synthesis.