k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations

@article{Qin2019ktND,
  title={k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations},
  author={Chen Qin and Jo Schlemper and Jinming Duan and Gavin Seegoolam and Anthony N. Price and Joseph V. Hajnal and Daniel Rueckert},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.09425}
}
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach for dynamic MR image reconstruction, termed k-t NEXT (k-t NEtwork with X-f Transform). In particular, inspired by traditional methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true signals from aliased signals in x-f domain to… 
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