  Magnetic resonance imaging (MRI) is a potent diagnostic tool, but suffers
from long examination times. To accelerate the process, modern MRI machines
typically utilize multiple coils that acquire sub-sampled data in parallel.
Data-driven reconstruction approaches, in particular diffusion models, recently
achieved remarkable success in reconstructing these data, but typically rely on
estimating the coil sensitivities in an off-line step. This suffers from
potential movement and misalignment artifacts and limits the application to
Cartesian sampling trajectories. To obviate the need for off-line sensitivity
estimation, we propose to jointly estimate the sensitivity maps with the image.
In particular, we utilize a diffusion model -- trained on magnitude images only
-- to generate high-fidelity images while imposing spatial smoothness of the
sensitivity maps in the reverse diffusion. The proposed approach demonstrates
consistent qualitative and quantitative performance across different
sub-sampling patterns. In addition, experiments indicate a good fit of the
estimated coil sensitivities.
