Chang Gao1,2, Vahid Ghodrati1,2, Dylan Nguyen3, Marcel Dominik Nickel4, Thomas Vahle4, Brian Dale5, 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
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
We developed a 3D residual generative adversarial network to remove streaking artifacts of undersampled stack-of-radial MRI. We have shown the feasibility of the network with 3.1x to 6.3x acceleration factors and 6 different echo times.
Figure 4. Example image quality of the single-echo test results with 6.3x to 3.1x accelerator factors. The input shows the undersampled images, the output shows the images after our network destreaking, and the target shows the fully-sampled ground truth images.
Figure 5. Example image quality of the multi-echo test results of a 4.2x acceleration factor. The input shows the undersampled images, the output shows the images after our network destreaking, and the target shows the fully-sampled ground truth images.
