  Long scan duration remains a challenge for high-resolution MRI. Deep learning
has emerged as a powerful means for accelerated MRI reconstruction by providing
data-driven regularizers that are directly learned from data. These data-driven
priors typically remain unchanged for future data in the testing phase once
they are learned during training. In this study, we propose to use a transfer
learning approach to fine-tune these regularizers for new subjects using a
self-supervision approach. While the proposed approach can compromise the
extremely fast reconstruction time of deep learning MRI methods, our results on
knee MRI indicate that such adaptation can substantially reduce the remaining
artifacts in reconstructed images. In addition, the proposed approach has the
potential to reduce the risks of generalization to rare pathological
conditions, which may be unavailable in the training data.
