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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