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Found 5 papers, 5 papers with code
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Instead of the target transformation from undersampled to fully-sampled data required for MRI reconstruction, common diffusion priors are trained to learn a task-agnostic transformation from an asymptotic start-point of Gaussian noise onto the finite end-point of fully-sampled data.
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality.
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
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.
Federated Learning of Generative Image Priors for MRI Reconstruction
Specificity in the prior is preserved via a mapper subnetwork that produces site-specific latents.
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency.
