  Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing
under-sampled MR images, offering the potential to reduce scan times. Classical
techniques minimize a regularized least-squares cost function using an
expensive iterative optimization procedure. Recently, deep learning models have
been developed that model the iterative nature of classical techniques by
unrolling iterations in a neural network. While exhibiting superior
performance, these methods require large quantities of ground-truth images and
have shown to be non-robust to unseen data. In this paper, we explore a novel
strategy to train an unrolled reconstruction network in an unsupervised fashion
by adopting a loss function widely-used in classical optimization schemes. We
demonstrate that this strategy achieves lower loss and is computationally cheap
compared to classical optimization solvers while also exhibiting superior
robustness compared to supervised models. Code is available at
https://github.com/alanqrwang/HQSNet.
