Article detail · 2022
Federated Learning of Generative Image Priors for MRI Reconstruction
Journal
IEEE Transactions on Medical Imaging- Year
- 2022
- Type
- article
Data source split
- YÖKSİS venue IEEE Transactions on Medical Imaging
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
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 employ conditional reconstruction models to map from undersampled to fully-sampled acquisitions via explicit knowledge of the accelerated 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 patient privacy, performance and flexibility in multi-site collaborations, here we introduce Federated learning of Generative IMage Priors (FedGIMP) for MRI reconstruction. FedGIMP leverages a two-stage approach: cross-site learning of a generative MRI prior, and prior adaptation following 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. A novel mapper subnetwork produces site-specific latents to maintain specificity in the prior. During inference, the prior is first combined with subject-specific imaging operators to enable reconstruction, and it is then adapted to individual cross-sections by minimizing a data-consistency loss. Comprehensive experiments on multi-institutional datasets clearly demonstrate enhanced performance of FedGIMP against both centralized and FL methods based on conditional models.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
115 citations
OpenAlex cited_by_count (cache / database)
23 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Unsupervised Medical Image Translation With Adversarial Diffusion Models 2023
- Adaptive diffusion priors for accelerated MRI reconstruction 2023
- BolT: Fused window transformers for fMRI time series analysis 2023
- Self-consistent recursive diffusion bridge for medical image translation 2025
- One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis 2024
- Semi-Supervised Learning of MRI Synthesis Without Fully-Sampled Ground Truths 2022
- Parallel-stream fusion of scan-specific and scan-general priors for learning deep MRI reconstruction in low-data regimes 2023
- A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis 2024
- Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction 2026
- A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications 2025