Keywords: Image Reconstruction, Image ReconstructionLearning-based MRI reconstruction is commonly performed using non-adaptive models with frozen weights during inference. Non-adaptive conditional models poorly generalize across variable imaging operators, whereas non-adaptive unconditional models poorly generalize across variations in the image distribution. Here, we introduce a novel adaptive method, AdaDiff, that trains an unconditional diffusion prior for high-fidelity image generations and adapts the prior during inference for improved generalization. AdaDiff outperforms state-of-the-art baselines both visually and quantitatively.
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