  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.
