  MRI is a widely used ionization-free soft-tissue imaging modality, often
employed repeatedly over a patient's lifetime. However, prolonged scanning
durations, among other issues, can limit availability and accessibility. In
this work, we aim to substantially reduce scan times by leveraging prior scans
of the same patient. These prior scans typically contain considerable shared
information with the current scan, thereby enabling higher acceleration rates
when appropriately utilized. We propose a prior informed reconstruction method
with a trained diffusion model in conjunction with data-consistency steps. Our
method can be trained with unlabeled image data, eliminating the need for a
dataset of either k-space measurements or paired longitudinal scans as is
required of other learning-based methods. We demonstrate superiority of our
method over previously suggested approaches in effectively utilizing prior
information without over-biasing prior consistency, which we validate on both
an open-source dataset of healthy patients as well as several longitudinal
cases of clinical interest.
