Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network
Chang Gao1,2, Vahid Ghodrati1,2, Shu-Fu Shih1,3, Holden H. Wu1,2,3, Yongkai Liu1,2, Marcel Dominik Nickel4, Thomas Vahle4, Brian Dale5, Victor Sai1, Ely Felker1, Qi Miao1, Xiaodong Zhong6, and Peng Hu1,2
1Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States, 2Department of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States, 3Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, 4MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, 5MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Cary, NC, United States, 6MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States
Undersampling is desired to reduce scan time but can cause streaking artifacts in stack-of-radial imaging. State-of-the-art deep neural networks such as the U-Net can be trained in a supervised manner to remove streaking artifacts but produce blurred images and loss of image details. Therefore, we developed and trained a 3D generative adversarial network to preserve perceptual image sharpness while removing streaking artifacts. The network used a combination of adversarial loss, L2 loss and structural similarity index loss. We demonstrated the feasibility of the proposed network for removing streaking artifacts and preserving perceptual image sharpness.
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