A k-space transformer network for undersampled radial MRI
Chang Gao1,2, Shu-Fu Shih1,3, Vahid Ghodrati1,2, Paul Finn1,2, Peng Hu1,2, and Xiaodong Zhong4
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 R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States
Deep learning-based undersampled MRI reconstruction generally requires the k-space data consistency term to constrain the output. However, this requires gridding onto a Cartesian basis for radial data, which slows down the training process profoundly and may even make it impractical. To avoid the repeated gridding process in training, we developed a transformer network to directly predict unacquired radial k-space spokes. The developed network was evaluated in vivo, accurately predicted the unacquired k-space spokes and generated better image intensity and less streaking artifacts compared to the undersampled images.
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