To reduce the usage of gadolinium-based contrast agents (GBCAs), we proposed a deep learning based multi-inputs network (CE-Net) for contrast enhanced T1-weighted MR image synthesis based on pre-contrast T1-weighted and T2-weighted images in nasopharyngeal carcinoma (NPC) cases. When compared with multi-channel input methods, the proposed CE-Net has the ability to extract information from each input modalities separately. Supervision and multi-scale strategies are also applied in the proposed network. Quantitative and qualitative results show that our proposed CE-Net could achieve better performance when compared with the newly proposed Hi-Net and its extensions.
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