Inspired by two open questions of dynamic MRI reconstruction, we propose a novel voxel-wise attention network for temporal modeling for the undersampled reconstruction. The voxel-wise design of the network enables voxel-wise training, and we further propose a two-stage training scheme that pretrains the network with voxel-wise simulated data when dynamics are easy to obtain with physical models. With a factor of 12 undersampling, our proposed model outperforms other reconstructions with higher PSNR and better fMRI performance.
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