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Abstract #3637

Image Reconstruction Using Generative Adversarial Networks with MR-Specific Feature Map

Ruiying Liu1, Hongyu Li1, Dong Liang2, Xiaojuan Li3, Chaoyi Zhang1, Peizhou Zhou1, Leslie Ying1, and Xiaoliang Zhang4
1Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, 2Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS,, Shenzhen, China, 3Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, 4Department of Biomedical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States

Deep learning methods have demonstrated great potential in image reconstruction due to its ability to learn the non-linearity relationship between the undersampled k-space data and the corresponding desired image. Among these methods, Generative Adversarial Networks (GANs) is known to reconstruct images that are sharper and more realistic-looking. In this abstract, we study whether an MR-specific feature map that is trained on a large number of MRI images and used in the loss function can improve the GAN-based reconstruction. We demonstrate that the MR-specific feature map is superior to the pre-trained feature map typically used for GAN-based reconstruction.

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