  High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a
powerful clinical tool for imaging moving structures as well as to reveal and
quantify other physical and physiological dynamics. The low speed of MRI
necessitates acceleration methods such as deep learning reconstruction from
under-sampled data. However, the massive size of many dynamic MRI problems
prevents deep learning networks from directly exploiting global temporal
relationships. In this work, we show that by applying deep neural networks
inside a priori calculated temporal feature spaces, we enable deep learning
reconstruction with global temporal modeling even for image sequences with
>40,000 frames. One proposed variation of our approach using dilated
multi-level Densely Connected Network (mDCN) speeds up feature space coordinate
calculation by 3000x compared to conventional iterative methods, from 20
minutes to 0.39 seconds. Thus, the combination of low-rank tensor and deep
learning models not only makes large-scale dynamic MRI feasible but also
practical for routine clinical application.
