  Recent medical image reconstruction techniques focus on generating
high-quality medical images suitable for clinical use at the lowest possible
cost and with the fewest possible adverse effects on patients. Recent works
have shown significant promise for reconstructing MR images from sparsely
sampled k-space data using deep learning. In this work, we propose a technique
that rapidly estimates deep neural networks directly at reconstruction time by
fitting them on small adaptively estimated neighborhoods of a training set. In
brief, our algorithm alternates between searching for neighbors in a data set
that are similar to the test reconstruction, and training a local network on
these neighbors followed by updating the test reconstruction. Because our
reconstruction model is learned on a dataset that is in some sense similar to
the image being reconstructed rather than being fit on a large, diverse
training set, it is more adaptive to new scans. It can also handle changes in
training sets and flexible scan settings, while being relatively fast. Our
approach, dubbed LONDN-MRI, was validated on multiple data sets using deep
unrolled reconstruction networks. Reconstructions were performed at four fold
and eight fold undersampling of k-space with 1D variable-density random
phase-encode undersampling masks. Our results demonstrate that our proposed
locally-trained method produces higher-quality reconstructions compared to
models trained globally on larger datasets as well as other scan-adaptive
methods.
