DIMENSION: Dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training.
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi-supervised network training technique is developed to constrain the frequency domain information and the spatial domain information. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.
['Wang|Shanshan|S|https://orcid.org/0000-0002-0575-6523', 'Ke|Ziwen|Z|https://orcid.org/0000-0003-3298-8220', 'Cheng|Huitao|H|', 'Jia|Sen|S|', 'Ying|Leslie|L|', 'Zheng|Hairong|H|', 'Liang|Dong|D|']
dynamic MR imaging multi-supervised compressed sensing deep learning k-space prior