  Imputation of missing images via source-to-target modality translation can
improve diversity in medical imaging protocols. A pervasive approach for
synthesizing target images involves one-shot mapping through generative
adversarial networks (GAN). Yet, GAN models that implicitly characterize the
image distribution can suffer from limited sample fidelity. Here, we propose a
novel method based on adversarial diffusion modeling, SynDiff, for improved
performance in medical image translation. To capture a direct correlate of the
image distribution, SynDiff leverages a conditional diffusion process that
progressively maps noise and source images onto the target image. For fast and
accurate image sampling during inference, large diffusion steps are taken with
adversarial projections in the reverse diffusion direction. To enable training
on unpaired datasets, a cycle-consistent architecture is devised with coupled
diffusive and non-diffusive modules that bilaterally translate between two
modalities. Extensive assessments are reported on the utility of SynDiff
against competing GAN and diffusion models in multi-contrast MRI and MRI-CT
translation. Our demonstrations indicate that SynDiff offers quantitatively and
qualitatively superior performance against competing baselines.
