Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction
This addresses the need for privacy-preserving, flexible collaborative training in medical imaging, allowing institutions to use tailored models while improving generalization, though it is incremental as it builds on existing federated learning and generative model concepts.
The paper tackles the problem of poor generalization in MRI reconstruction due to limited local datasets by proposing FedGAT, a model-agnostic federated learning technique that collaboratively trains a global generative prior and uses synthetic data for decentralized augmentation, resulting in outperforming state-of-the-art FL baselines in within- and cross-site reconstruction performance.
While learning-based models hold great promise for MRI reconstruction, single-site models trained on limited local datasets often show poor generalization. This has motivated collaborative training across institutions via federated learning (FL)-a privacy-preserving framework that aggregates model updates instead of sharing raw data. Conventional FL requires architectural homogeneity, restricting sites from using models tailored to their resources or needs. To address this limitation, we propose FedGAT, a model-agnostic FL technique that first collaboratively trains a global generative prior for MR images, adapted from a natural image foundation model composed of a variational autoencoder (VAE) and a transformer that generates images via spatial-scale autoregression. We fine-tune the transformer module after injecting it with a lightweight site-specific prompting mechanism, keeping the VAE frozen, to efficiently adapt the model to multi-site MRI data. In a second tier, each site independently trains its preferred reconstruction model by augmenting local data with synthetic MRI data from other sites, generated by site-prompting the tuned prior. This decentralized augmentation improves generalization while preserving privacy. Experiments on multi-institutional datasets show that FedGAT outperforms state-of-the-art FL baselines in both within- and cross-site reconstruction performance under model-heterogeneous settings.