Neural network architectures have recently been proposed for reconstruction of undersampled MR acquisitions. These networks contain a large number of free parameters that typically have to be trained on orders-of-magnitude larger sets of fully-sampled MRI data. In practice, however, large datasets comprising thousands of images are rare. Here, we propose a transfer-learning approach to address the problem of data scarcity in training deep networks for accelerated MRI. Results show that 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 MR images.