  Deep neural networks (DNNs) have recently found emerging use in accelerated
MRI reconstruction. DNNs typically learn data-driven priors from large datasets
constituting pairs of undersampled and fully-sampled acquisitions. Acquiring
such large datasets, however, might be impractical. To mitigate this
limitation, we propose a few-shot learning approach for accelerated MRI that
merges subject-driven priors obtained via physical signal models with
data-driven priors obtained from a few training samples. Demonstrations on
brain MR images from the NYU fastMRI dataset indicate that the proposed
approach requires just a few samples to outperform traditional parallel imaging
and DNN algorithms.
