U$^2$MRPD: Unsupervised undersampled MRI reconstruction by prompting a
  large latent diffusion model
Ziqi Gao S. Kevin Zhou
Implicit visual knowledge in a large latent diffusion model (LLDM)
pre-trained on natural images is rich and hypothetically universal to natural
and medical images. To test this hypothesis, we introduce a novel framework for
Unsupervised Undersampled MRI Reconstruction by Prompting a pre-trained large
latent Diffusion model ( U$^2$MRPD). Existing data-driven, supervised
undersampled MRI reconstruction networks are typically of limited
generalizability and adaptability toward diverse data acquisition scenarios;
yet U$^2$MRPD supports image-specific MRI reconstruction by prompting an LLDM
with an MRSampler tailored for complex-valued MRI images. With any
single-source or diverse-source MRI dataset, U$^2$MRPD's performance is further
boosted by an MRAdapter while keeping the generative image priors intact.
Experiments on multiple datasets show that U$^2$MRPD achieves comparable or
better performance than supervised and MRI diffusion methods on in-domain
datasets while demonstrating the best generalizability on out-of-domain
datasets. To the best of our knowledge, U$^2$MRPD is the {\bf first}
unsupervised method that demonstrates the universal prowess of a LLDM, %trained
on magnitude-only natural images in medical imaging, attaining the best
adaptability for both MRI database-free and database-available scenarios and
generalizability towards out-of-domain data.