  Magnetic Resonance (MR) image reconstruction from under-sampled acquisition
promises faster scanning time. To this end, current State-of-The-Art (SoTA)
approaches leverage deep neural networks and supervised training to learn a
recovery model. While these approaches achieve impressive performances, the
learned model can be fragile on unseen degradation, e.g. when given a different
acceleration factor. These methods are also generally deterministic and provide
a single solution to an ill-posed problem; as such, it can be difficult for
practitioners to understand the reliability of the reconstruction. We introduce
DiffuseRecon, a novel diffusion model-based MR reconstruction method.
DiffuseRecon guides the generation process based on the observed signals and a
pre-trained diffusion model, and does not require additional training on
specific acceleration factors. DiffuseRecon is stochastic in nature and
generates results from a distribution of fully-sampled MR images; as such, it
allows us to explicitly visualize different potential reconstruction solutions.
Lastly, DiffuseRecon proposes an accelerated, coarse-to-fine Monte-Carlo
sampling scheme to approximate the most likely reconstruction candidate. The
proposed DiffuseRecon achieves SoTA performances reconstructing from raw
acquisition signals in fastMRI and SKM-TEA. Code will be open-sourced at
www.github.com/cpeng93/DiffuseRecon.
