Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction

TL;DR

This paper introduces FedGAT, a federated learning approach for MRI reconstruction that uses a model-agnostic generative prior to improve cross-site generalization without sharing raw data.

Contribution

FedGAT presents a novel federated learning framework that trains a global generative prior adaptable to different models and enhances local MRI reconstruction through synthetic data augmentation.

Findings

FedGAT outperforms existing federated learning methods in MRI reconstruction accuracy.

The approach improves cross-site generalization in multi-institutional datasets.

Synthetic data augmentation enhances local model performance without compromising privacy.

Abstract

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.