  Deep neural networks have emerged as very successful tools for image
restoration and reconstruction tasks. These networks are often trained
end-to-end to directly reconstruct an image from a noisy or corrupted
measurement of that image. To achieve state-of-the-art performance, training on
large and diverse sets of images is considered critical. However, it is often
difficult and/or expensive to collect large amounts of training images.
Inspired by the success of Data Augmentation (DA) for classification problems,
in this paper, we propose a pipeline for data augmentation for accelerated MRI
reconstruction and study its effectiveness at reducing the required training
data in a variety of settings. Our DA pipeline, MRAugment, is specifically
designed to utilize the invariances present in medical imaging measurements as
naive DA strategies that neglect the physics of the problem fail. Through
extensive studies on multiple datasets we demonstrate that in the low-data
regime DA prevents overfitting and can match or even surpass the state of the
art while using significantly fewer training data, whereas in the high-data
regime it has diminishing returns. Furthermore, our findings show that DA can
improve the robustness of the model against various shifts in the test
distribution.
