  Multi-institutional efforts can facilitate training of deep MRI
reconstruction models, albeit privacy risks arise during cross-site sharing of
imaging data. Federated learning (FL) has recently been introduced to address
privacy concerns by enabling distributed training without transfer of imaging
data. Existing FL methods for MRI reconstruction employ conditional models to
map from undersampled to fully-sampled acquisitions via explicit knowledge of
the imaging operator. Since conditional models generalize poorly across
different acceleration rates or sampling densities, imaging operators must be
fixed between training and testing, and they are typically matched across
sites. To improve generalization and flexibility in multi-institutional
collaborations, here we introduce a novel method for MRI reconstruction based
on Federated learning of Generative IMage Priors (FedGIMP). FedGIMP leverages a
two-stage approach: cross-site learning of a generative MRI prior, and
subject-specific injection of the imaging operator. The global MRI prior is
learned via an unconditional adversarial model that synthesizes high-quality MR
images based on latent variables. Specificity in the prior is preserved via a
mapper subnetwork that produces site-specific latents. During inference, the
prior is combined with subject-specific imaging operators to enable
reconstruction, and further adapted to individual test samples by minimizing
data-consistency loss. Comprehensive experiments on multi-institutional
datasets clearly demonstrate enhanced generalization performance of FedGIMP
against site-specific and federated methods based on conditional models, as
well as traditional reconstruction methods.
