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
multisupervised network training technique is developed to constrain the
frequency domain information and reconstruction results at different levels.
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.

本论文提出了一种综合应用 k 空间和空间先验知识的动态 MRI 成像方法，采用多监督网络训练技术，可以在更短的时间内获得比传统方法和现有技术更好的重建结果。