Gokberk Elmas

Alper Güngör, Salman UH Dar, Şaban Öztürk et al.

Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditional models are trained with knowledge of the imaging operator, they can show poor generalization across variable operators. Unconditional models instead learn generative image priors decoupled from the operator to improve reliability against domain shifts related to the imaging operator. Recent diffusion models are particularly promising given their high sample fidelity. Nevertheless, inference with a static image prior can perform suboptimally. Here we propose the first adaptive diffusion prior for MRI reconstruction, AdaDiff, to improve performance and reliability against domain shifts. AdaDiff leverages an efficient diffusion prior trained via adversarial mapping over large reverse diffusion steps. A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss. Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff outperforms competing conditional and unconditional methods under domain shifts, and achieves superior or on par within-domain performance.

Valiyeh A. Nezhad, Gokberk Elmas, Bilal Kabas et al.

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.

Gokberk Elmas, Salman UH Dar, Yilmaz Korkmaz et al.

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.