  Purpose: Neural networks have received recent interest for reconstruction of
undersampled MR acquisitions. Ideally network performance should be optimized
by drawing the training and testing data from the same domain. In practice,
however, large datasets comprising hundreds of subjects scanned under a common
protocol are rare. The goal of this study is to introduce a transfer-learning
approach to address the problem of data scarcity in training deep networks for
accelerated MRI.
  Methods: Neural networks were trained on thousands of samples from public
datasets of either natural images or brain MR images. The networks were then
fine-tuned using only few tens of brain MR images in a distinct testing domain.
Domain-transferred networks were compared to networks trained directly in the
testing domain. Network performance was evaluated for varying acceleration
factors (2-10), number of training samples (0.5-4k) and number of fine-tuning
samples (0-100).
  Results: The proposed approach achieves successful domain transfer between MR
images acquired with different contrasts (T1- and T2-weighted images), and
between natural and MR images (ImageNet and T1- or T2-weighted images).
Networks obtained via transfer-learning using only tens of images in the
testing domain achieve nearly identical performance to networks trained
directly in the testing domain using thousands of images.
  Conclusion: The proposed approach might facilitate the use of neural networks
for MRI reconstruction without the need for collection of extensive imaging
datasets.
