Abstract #3515

A k-space transformer network for undersampled radial MRI

Chang Gao^1,2, Shu-Fu Shih^1,3, Vahid Ghodrati^1,2, Paul Finn^1,2, Peng Hu^1,2, 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

Synopsis

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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