Accelerated MRI Reconstruction using Adaptive Diffusion Probabilistic Networks

Learning-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.