  Fast and accurate reconstruction of magnetic resonance (MR) images from
under-sampled data is important in many clinical applications. In recent years,
deep learning-based methods have been shown to produce superior performance on
MR image reconstruction. However, these methods require large amounts of data
which is difficult to collect and share due to the high cost of acquisition and
medical data privacy regulations. In order to overcome this challenge, we
propose a federated learning (FL) based solution in which we take advantage of
the MR data available at different institutions while preserving patients'
privacy. However, the generalizability of models trained with the FL setting
can still be suboptimal due to domain shift, which results from the data
collected at multiple institutions with different sensors, disease types, and
acquisition protocols, etc. With the motivation of circumventing this
challenge, we propose a cross-site modeling for MR image reconstruction in
which the learned intermediate latent features among different source sites are
aligned with the distribution of the latent features at the target site.
Extensive experiments are conducted to provide various insights about FL for MR
image reconstruction. Experimental results demonstrate that the proposed
framework is a promising direction to utilize multi-institutional data without
compromising patients' privacy for achieving improved MR image reconstruction.
Our code will be available at https://github.com/guopengf/FLMRCM.
