  Deep neural networks have been extensively studied for undersampled MRI
reconstruction. While achieving state-of-the-art performance, they are trained
and deployed specifically for one anatomy with limited generalization ability
to another anatomy. Rather than building multiple models, a universal model
that reconstructs images across different anatomies is highly desirable for
efficient deployment and better generalization. Simply mixing images from
multiple anatomies for training a single network does not lead to an ideal
universal model due to the statistical shift among datasets of various
anatomies, the need to retrain from scratch on all datasets with the addition
of a new dataset, and the difficulty in dealing with imbalanced sampling when
the new dataset is further of a smaller size. In this paper, for the first
time, we propose a framework to learn a universal deep neural network for
undersampled MRI reconstruction. Specifically, anatomy-specific instance
normalization is proposed to compensate for statistical shift and allow easy
generalization to new datasets. Moreover, the universal model is trained by
distilling knowledge from available independent models to further exploit
representations across anatomies. Experimental results show the proposed
universal model can reconstruct both brain and knee images with high image
quality. Also, it is easy to adapt the trained model to new datasets of smaller
size, i.e., abdomen, cardiac and prostate, with little effort and superior
performance.
