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Abstract #3502

CE-Net: multi-inputs contrast enhancement network for nasopharyngeal carcinoma contrast enhanced T1-weighted MR synthesis

Wen Li1, Ge Ren1, Tian Li1, Haonan Xiao1, Francis Kar-ho Lee2, Kwok-hung Au2, and Jing Cai1
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China, 2Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China

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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