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.
This abstract and the presentation materials are available to members only; a login is required.